Date: (Wed) Jun 15, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- #NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
# '(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))' # No
# '(glbObsAll[, "Q109244"] != "")' # NA
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "Yes"))' # Yes
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q115611.fctr" # choose from c(NULL : default, "<category_feat>")

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(4, 6, 7, 8, 9, 10, 16, 32, 64, 128, 253) # accuracy(8) = 0.5648
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    # glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
    glbMdlFamilies[["All.X"]] <- c("glmnet")
    # glbMdlFamilies[["All.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     , "lda" # error: model fit failed for Fold1.Rep1: parameter=none Error in lda.default(x, grouping, ...)
    #     ,"lda2" # error: There were missing values in resampled performance measures.
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
    #     , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))

    # glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet")
    # glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     # , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     # , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !
    #     , "svmRadial" # Error in .local(object, ...) : test vector does not match model !
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlSequential <- c(NULL
                      , "All.X#zv.pca#rcv#glmnet"
                      )

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()

# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame() # alpha shd be <= 1.0 ALWAYS
    ,data.frame(parameter = "alpha",  vals = "0.325 0.550 0.775 0.9 1.000")
    ,data.frame(parameter = "lambda", vals = "1.034113e-03 4.799925e-03 2.227928e-02 0.04 0.06")
                        ) # max.Accuracy.OOB = 0.7875648 @ 0.55   0.04
# AllX_nzv_rcv_glmnetTuneParams <- rbind(data.frame() 
#     ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000") 
#     ,data.frame(parameter = "lambda", vals = "1.842462e-02 0.03287977 0.04733492 0.06179007 0.07624522")) #  max.Accuracy.OOB = 0.7875648 @ 0.55 0.06179007 @ 0.55 0.04733492 @ 0.775 0.03287977 @ 1 0.01842462
# AllX_zvpca_rcv_glmnetTuneParams <- rbind(data.frame() 
#     ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000") 
#     ,data.frame(parameter = "lambda", vals = "1.847495e-02 0.02 0.03296959 0.04 0.05")) #  max.Accuracy.OOB = 0.7927461 @ 1 0.01847495
# #                  0.7875648 @ 0.775 0.03296959
# 
glbMdlTuneParams <- rbind(glbMdlTuneParams
    ,cbind(data.frame(mdlId = "All.X##rcv#glmnet"),     AllX__rcv_glmnetTuneParams)
    # ,cbind(data.frame(mdlId = "All.X#nzv#rcv#glmnet"),  AllX_nzv_rcv_glmnetTuneParams)
    # ,cbind(data.frame(mdlId = "All.X#zv.pca#rcv#glmnet"),
    #                                                     AllX_zvpca_rcv_glmnetTuneParams)
)

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# bagEarthTuneParams <- rbind(data.frame()
#                         ,data.frame(parameter = "degree", vals = "1")
#                         ,data.frame(parameter = "nprune", vals = "256")
#                         )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
#                                      bagEarthTuneParams))

# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list(NULL # NULL # : default
    # ,"All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 c(NULL)))
    # #                                 c("zv.YeoJohnson.pca")))
    # ,"RFE.X" = list("glmnet" = union(setdiff(pkgPreprocMethods,
    #                                         c("knnImpute", "bagImpute", "medianImpute")),
    #                                 c(NULL)))
    #                                 # c("zv.pca.spatialSign")))
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
#                                                     "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
                           "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
              "min.elapsedtime.everything", 
              # "min.aic.fit", 
              "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL # NULL : default #"auto"
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )
glbMdlEnsembleSampleMethods <- c("boot", "boot632", "cv", "repeatedcv"
               # , "LOOCV" # tuneLength * nrow(fitDF) # way too many models
               , "LGOCV"
               , "adaptive_cv" # crashed for Q109244No
               # , "adaptive_boot"  #error: adaptive$min should be less than 3
               # , "adaptive_LGOCV" #error: adaptive$min should be less than 3
               )


# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- NULL #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- # NULL #: default
    c("Q109244NA_Ensemble_cnk03_rest_out_fin.csv") 
    # c("Q109244No_AllXpreProc_cnk03_rest_out_fin.csv") 
    # c("Votes_Ensemble_cnk06_out_fin.csv") 


glbOut <- list(pfx = "Q109244Yes_AllX_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "cluster.data" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Q109244Yes_AllX_cnk01_manage.missing.data_manage.missing.data.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##          label step_major step_minor label_minor   bgn end elapsed
## 1 cluster.data          1          0           0 9.273  NA      NA

Step 1.0: cluster data

chunk option: eval=

Step 1.0: cluster data

Step 1.0: cluster data

```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

## Loading required package: proxy
## 
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## The following object is masked from 'package:base':
## 
##     as.matrix
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr
## Loading required package: ggdendro
## [1] "Clustering features: "
## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero
##               abs.cor.y
## Q121699.fctr 0.06186040
## Q114517.fctr 0.06233932
## Q124122.fctr 0.06976947
## Q114386.fctr 0.07613008
## Q114152.fctr 0.07783674
## [1] "    .rnorm abs(cor): 0.0102"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.4974"
## Loading required package: lazyeval
##   Hhold.fctr .clusterid Hhold.fctr.clusterid   D  R  .entropy .knt
## 1          N          1                  N_1  40  9 0.4769183   49
## 2        MKn          1                MKn_1  97 26 0.5157821  123
## 3        MKy          1                MKy_1 186 43 0.4829775  229
## 4        PKn          1                PKn_1  45  5 0.3250830   50
## 5        PKy          1                PKy_1  10  1 0.3046361   11
## 6        SKn          1                SKn_1 325 92 0.5276960  417
## 7        SKy          1                SKy_1  39  7 0.4264615   46
## [1] "glbObsAll$Hhold.fctr Entropy: 0.4937 (99.2605 pct)"
## [1] "Category: N"
## [1] "max distance(0.9741) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4156    5186          D          N           NA           NA           NA
## 4978    6219          D          N           NA           NA           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4156           NA           NA           NA           NA           NA
## 4978           NA           Pt          Yes           No           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4156           NA           NA           NA           NA          Yes
## 4978           No          Yes           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4156          Art          Yes           NA           NA           No
## 4978           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4156           NA       Giving           NA           No          Yes
## 4978           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4156          Yes           Id           No Standard hours  Cool headed
## 4978           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4156          Yes           NA           No           NA           NA
## 4978           NA           NA          Yes           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4156           NA           NA           NA           NA           NA
## 4978           NA           NA          Yes        Start           No
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4156           NA           NA           NA           NA           NA
## 4978           No           Cs          Yes           No          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4156           NA           NA           NA           NA           NA
## 4978          Yes           No          TMI           NA          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4156           NA           NA           NA           NA           NA
## 4978        Tunes   Technology           No          Yes           No
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4156           NA           NA           NA           NA           NA
## 4978           NA          Yes           NA          Yes          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4156           No          Yes Risk-friendly         Yes!           NA
## 4978           NA          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4156           NA          Yes    In-person           NA           No
## 4978           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4156          Yes           NA           Yy           NA           NA
## 4978           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4156           NA           NA          Yes          Yes          Yes
## 4978           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4156           No           No           NA          Yes          Yes
## 4978           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4156           NA           NA           NA           NA           NA
## 4978           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4156           NA           NA          Yes          NA          NA
## 4978           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4156          NA          NA         Yes         Yes         Yes
## 4978          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4156         Yes          NA         Yes
## 4978          NA          NA          NA
## [1] "min distance(0.9626) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1348    1671          D          N           NA           No           No
## 4406    5498          D          N           NA           No           No
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1348          Yes           Pc           No           No           No
## 4406           No           Pc          Yes          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1348           No          Yes          Yes          Yes           No
## 4406           No           No          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1348          Art          Yes    Try first           No          Yes
## 4406      Science           No  Study first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1348           No       Giving           No           No          Yes
## 4406           No       Giving           No           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 1348           No           Id          Yes Standard hours  Cool headed
## 4406           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1348           No        Happy           No          Yes           No
## 4406           No        Happy          Yes          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1348          Yes         P.M.          Yes          End          Yes
## 4406           No         P.M.          Yes           NA          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1348           No           Cs          Yes           No           No
## 4406          Yes           NA          Yes          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1348          Yes          Yes          TMI          Yes           No
## 4406           No          Yes          TMI          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1348        Tunes       People          Yes          Yes          Yes
## 4406        Tunes   Technology          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1348           No          Yes   Supportive          Yes          Mac
## 4406           No           No   Supportive           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 1348           No          Yes Risk-friendly         Yes!          Yes
## 4406           NA          Yes      Cautious       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1348    Socialize          Yes    In-person           No           No
## 4406        Space           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1348          Yes          Yes           Gr          Yes           No
## 4406           NA          Yes           Yy          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1348          Yes          Yes          Yes          Yes          Yes
## 4406          Yes          Yes          Yes          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1348          Yes           No           No          Yes           No
## 4406          Yes           No          Yes          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1348          Own     Optimist          Mom           No           No
## 4406          Own     Optimist          Mom          Yes          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1348          Yes          Yes           No        Nope          No
## 4406          Yes          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1348         Yes          No         Yes         Yes         Yes
## 4406          No         Yes         Yes          No         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1348         Yes         Yes          No
## 4406  Only-child         Yes          No
## [1] "Category: MKn"
## [1] "max distance(0.9742) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3967    4944          D        MKn          Yes          Yes          Yes
## 5755     969       <NA>        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3967           No           NA           NA           NA           NA
## 5755           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3967           NA           NA           No           NA           NA
## 5755           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3967           NA           No  Study first           No           No
## 5755           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3967           No    Receiving           No          Yes           No
## 5755           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 3967           No           Pr          Yes    Odd hours   Hot headed
## 5755           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3967           No           NA          Yes          Yes          Yes
## 5755           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3967          Yes         P.M.           NA           NA          Yes
## 5755           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3967           No           Me           NA           NA           NA
## 5755           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3967           NA           NA   Mysterious           NA          Yes
## 5755           NA           NA           NA           No           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3967        Tunes       People           No           NA          Yes
## 5755         Talk       People           No          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3967           NA           NA           NA           NA           NA
## 5755          Yes          Yes   Supportive           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 3967           No          Yes Risk-friendly         Yes!           NA
## 5755          Yes          Yes      Cautious       Umm...          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3967        Space           No    In-person          Yes           No
## 5755        Space          Yes    In-person          Yes           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3967          Yes           NA           NA           NA           NA
## 5755          Yes          Yes           Gr          Yes          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3967           NA           NA           NA           NA           NA
## 5755           No          Yes          Yes          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3967           NA           NA           NA           NA           NA
## 5755          Yes           No           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3967           NA           NA           NA           NA           NA
## 5755         Rent    Pessimist          Dad          Yes           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3967           NA           NA           NA          NA          NA
## 5755          Yes          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3967          NA          NA          NA          NA          NA
## 5755         Yes         Yes          No         Yes          No
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3967          NA          NA          NA
## 5755         Yes         Yes         Yes
## [1] "min distance(0.9614) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1370    1704          D        MKn           No           No           No
## 3679    4588          D        MKn          Yes           No          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1370           No           Pt          Yes           No           No
## 3679           No           Pt           No          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1370           No          Yes           No           No          Yes
## 3679           No          Yes           No           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1370      Science           No  Study first           No          Yes
## 3679          Art           NA  Study first           No          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1370           No       Giving          Yes          Yes           No
## 3679           No       Giving           No          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 1370           No           Id           No      Odd hours  Cool headed
## 3679           No           Id           No Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1370           No        Happy          Yes          Yes           No
## 3679          Yes        Happy          Yes          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1370          Yes         A.M.           No        Start          Yes
## 3679          Yes         P.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1370          Yes           Cs          Yes          Yes           No
## 3679          Yes           Me          Yes          Yes          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1370          Yes          Yes          TMI          Yes          Yes
## 3679          Yes          Yes          TMI          Yes          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1370        Tunes       People           No          Yes          Yes
## 3679        Tunes   Technology          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1370          Yes           No   Supportive           No          Mac
## 3679           No           No    Demanding           No           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 1370           No          Yes Risk-friendly       Umm...           No
## 3679          Yes          Yes      Cautious       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1370    Socialize           No    In-person           No           No
## 3679        Space           No    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1370          Yes          Yes           Yy          Yes           No
## 3679          Yes          Yes           Gr          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1370          Yes          Yes           No           No          Yes
## 3679           No           No          Yes          Yes           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1370           No           No           No          Yes           No
## 3679           No           No           No          Yes          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1370          Own     Optimist          Dad          Yes          Yes
## 3679          Own     Optimist          Dad           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1370           No          Yes          Yes      Check!          No
## 3679          Yes          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1370          No         Yes          No         Yes          No
## 3679         Yes         Yes          No         Yes         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1370         Yes         Yes          No
## 3679         Yes         Yes          No
## [1] "Category: MKy"
## [1] "max distance(0.9761) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 132      167          D        MKy           NA           NA           NA
## 4451    5552          D        MKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 132            NA           NA           NA           NA           NA
## 4451           No           Pc           No           No           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 132            NA           NA           NA           NA           NA
## 4451           No          Yes          Yes           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 132            NA           NA           NA           NA           NA
## 4451      Science          Yes           NA          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 132            NA           NA           NA           NA           NA
## 4451           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 132            NA           NA           NA             NA           NA
## 4451           NA           NA           NA Standard hours   Hot headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 132            NA           NA           NA           NA           NA
## 4451           No        Happy          Yes          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 132            NA           NA           NA           NA           NA
## 4451           No         A.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 132            NA           NA           NA           NA           NA
## 4451           No           Me           No          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 132            NA           NA           NA           NA           NA
## 4451          Yes           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 132            NA           NA           NA           No          Yes
## 4451           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 132            No          Yes    Demanding           No          Mac
## 4451           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 132           Yes          Yes      Cautious         Yes!           No
## 4451          Yes          Yes Risk-friendly         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 132         Space           No       Online          Yes          Yes
## 4451        Space           No    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 132            No          Yes           Gr          Yes           No
## 4451           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 132           Yes           No          Yes          Yes          Yes
## 4451           NA          Yes           No          Yes           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 132           Yes          Yes           No          Yes          Yes
## 4451           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 132           Own    Pessimist          Mom          Yes          Yes
## 4451           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 132           Yes           No          Yes      Check!          No
## 4451           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 132           No         Yes          No          No          No
## 4451          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 132          Yes         Yes         Yes
## 4451          NA          NA          NA
## [1] "min distance(0.9604) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4900    6123          D        MKy           NA          Yes           No
## 6791    6120       <NA>        MKy           NA          Yes           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4900           No           Pc          Yes          Yes           No
## 6791           NA           Pc           No           No           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4900           No          Yes           No           No          Yes
## 6791           No          Yes           No          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4900          Art           No    Try first          Yes          Yes
## 6791      Science           No    Try first          Yes          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4900          Yes       Giving           No          Yes           No
## 6791           No       Giving          Yes          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4900           No           Id          Yes      Odd hours  Cool headed
## 6791          Yes           Id           No Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4900          Yes        Happy          Yes          Yes           No
## 6791           No           NA           NA          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4900           No         P.M.          Yes        Start          Yes
## 6791           No         P.M.          Yes        Start           No
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4900          Yes           Me          Yes           No           No
## 6791           No           Cs          Yes          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4900          Yes          Yes          TMI           No          Yes
## 6791           No          Yes          TMI          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4900        Tunes   Technology           No          Yes           No
## 6791        Tunes   Technology           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4900          Yes          Yes    Demanding           No           PC
## 6791           NA           NA           NA           No          Mac
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4900          Yes          Yes     Cautious       Umm...           No
## 6791          Yes          Yes     Cautious       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4900        Space           No       Online           No          Yes
## 6791        Space           No    In-person          Yes          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4900          Yes          Yes           Yy          Yes           No
## 6791          Yes           No           Yy          Yes          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4900          Yes           No          Yes           No           No
## 6791          Yes           No           No          Yes           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4900           NA           NA           NA           NA           NA
## 6791           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4900           NA           NA           NA           NA           NA
## 6791           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4900           NA           NA           NA          NA          NA
## 6791           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4900          NA          NA          NA          NA          NA
## 6791          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4900          NA          NA          NA
## 6791          NA          NA          NA
## [1] "Category: PKn"
## [1] "max distance(0.9743) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 884     1099          D        PKn           NA           NA           NA
## 1645    2038          D        PKn           NA           NA          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 884            NA           NA           NA           NA           NA
## 1645           NA           Pc          Yes           NA           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 884            NA           NA           NA           NA           NA
## 1645           No          Yes          Yes           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 884            NA           NA           NA           NA           NA
## 1645           NA           NA  Study first           NA          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 884            NA           NA           NA           NA           NA
## 1645           No       Giving          Yes           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 884            NA           NA           NA             NA           NA
## 1645           NA           NA           NA Standard hours           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 884            NA           NA           NA           NA           NA
## 1645           NA           NA          Yes           NA           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 884            NA           NA           NA           NA           NA
## 1645           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 884            NA           NA           NA           NA           NA
## 1645           No           NA           NA           NA          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 884            NA           NA           NA           NA           NA
## 1645           No           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 884            NA           NA           NA           NA           NA
## 1645           NA           NA           No           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 884            NA           NA           NA           No          Mac
## 1645           NA          Yes           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 884            No          Yes Risk-friendly         Yes!           No
## 1645           No          Yes            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 884     Socialize          Yes    In-person           No          Yes
## 1645           NA           NA           NA           No           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 884           Yes          Yes           Yy          Yes           No
## 1645          Yes           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 884           Yes          Yes          Yes          Yes           No
## 1645           NA           No           NA          Yes           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 884           Yes          Yes           No           No           No
## 1645          Yes           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 884           Own           NA           NA          Yes           NA
## 1645          Own    Pessimist           NA          Yes          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 884            NA           NA           NA          NA          NA
## 1645           NA          Yes           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 884           NA          NA         Yes          No         Yes
## 1645          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 884          Yes          No          No
## 1645          NA          NA         Yes
## [1] "min distance(0.9617) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 993     1236          R        PKn           No          Yes           No
## 4354    5436          D        PKn          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 993            No           Pc          Yes           No           No
## 4354           No           Pc           No           No           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 993            No          Yes          Yes          Yes           NA
## 4354           No          Yes          Yes          Yes           No
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 993            NA           NA           NA           NA           NA
## 4354      Science           No  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 993            NA       Giving           No           No           NA
## 4354           No       Giving          Yes           No           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 993            NA           NA           NA           NA           NA
## 4354           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 993            NA           NA           NA           NA           NA
## 4354           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 993            NA         P.M.          Yes        Start          Yes
## 4354           NA         P.M.           No        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 993            No           Cs          Yes          Yes          Yes
## 4354          Yes           Me          Yes           NA          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 993           Yes          Yes          TMI           No           No
## 4354           No          Yes          TMI           No          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 993         Tunes       People           No          Yes          Yes
## 4354        Tunes       People           No          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 993            No           NA           NA           NA          Mac
## 4354           No           No   Supportive           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 993           Yes          Yes      Cautious       Umm...           NA
## 4354          Yes          Yes Risk-friendly         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 993         Space           No    In-person          Yes           No
## 4354    Socialize           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 993           Yes          Yes           Gr          Yes          Yes
## 4354           NA           No           Yy          Yes          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 993           Yes           No          Yes           No           No
## 4354          Yes           No          Yes           No          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 993            No           No          Yes           No          Yes
## 4354           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 993          Rent    Pessimist          Dad           NA          Yes
## 4354           NA     Optimist          Mom           NA          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 993           Yes          Yes          Yes      Check!          No
## 4354          Yes          Yes          Yes        Nope          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 993           No         Yes         Yes          No          No
## 4354          No         Yes         Yes          No          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 993          Yes         Yes         Yes
## 4354          NA          NA          NA
## [1] "Category: PKy"
## [1] "max distance(0.9685) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3626    4516          D        PKy           NA           NA           NA
## 6815    6244       <NA>        PKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3626           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3626           NA           NA           NA           NA           NA
## 6815           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3626           NA           NA           NA          Yes           No
## 6815           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3626           No           NA           NA           NA          Yes
## 6815           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 3626          Yes           Pr          Yes Standard hours  Cool headed
## 6815           NA           NA           NA             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3626           No        Happy          Yes           No           No
## 6815           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3626          Yes         A.M.           No          End          Yes
## 6815           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3626           No           Cs          Yes          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3626          Yes           No          TMI           No           No
## 6815           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3626         Talk       People           No          Yes          Yes
## 6815           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3626          Yes          Yes   Supportive           No          Mac
## 6815           NA           NA           NA           NA           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 3626          Yes          Yes      Cautious       Umm...           No
## 6815          Yes          Yes Risk-friendly       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3626        Space           No    In-person          Yes          Yes
## 6815        Space           No           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3626          Yes          Yes           Yy          Yes           No
## 6815           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3626          Yes          Yes           No           No           No
## 6815           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3626           No           No          Yes          Yes           No
## 6815           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3626         Rent     Optimist          Dad           No          Yes
## 6815           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3626          Yes          Yes          Yes      Check!          No
## 6815           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3626          No          No         Yes          No          No
## 6815          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3626         Yes          No          NA
## 6815          NA          NA          NA
## [1] "min distance(0.9623) pair:"
##     USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 501     626          R        PKy           NA           No          Yes
## 950    1181          D        PKy           NA          Yes          Yes
##     Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 501           No           Pc          Yes           No           No
## 950           No           Pc           No           No           No
##     Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 501           No          Yes          Yes          Yes           No
## 950           No          Yes           No          Yes          Yes
##     Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 501          Art          Yes  Study first          Yes          Yes
## 950      Science           No  Study first          Yes          Yes
##     Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 501           No       Giving           No          Yes           No
## 950          Yes       Giving           No          Yes          Yes
##     Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 501           No           Id           No      Odd hours  Cool headed
## 950           No           Id          Yes Standard hours   Hot headed
##     Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 501          Yes        Happy          Yes          Yes           No
## 950          Yes        Happy           No           No          Yes
##     Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 501           No         P.M.           No        Start          Yes
## 950          Yes         P.M.           No        Start          Yes
##     Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 501           No           Cs          Yes           No          Yes
## 950           No           Me          Yes           No          Yes
##     Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 501          Yes           No          TMI           No          Yes
## 950           No          Yes          TMI          Yes          Yes
##     Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 501        Tunes       People           No           No          Yes
## 950         Talk   Technology           No           No           No
##     Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 501          Yes           No   Supportive           No          Mac
## 950          Yes          Yes   Supportive           No          Mac
##     Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 501          Yes          Yes Risk-friendly       Umm...           No
## 950           No          Yes      Cautious       Umm...           No
##     Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 501        Space           No    In-person           No          Yes
## 950        Space           No       Online          Yes           No
##     Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 501          Yes           No           Yy          Yes          Yes
## 950          Yes          Yes           Gr          Yes          Yes
##     Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 501           No          Yes          Yes           No           No
## 950           No          Yes          Yes          Yes          Yes
##     Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 501           No           No          Yes          Yes           No
## 950           No          Yes          Yes          Yes           No
##     Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 501          Own     Optimist          Mom          Yes          Yes
## 950         Rent     Optimist          Mom           No          Yes
##     Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 501           No          Yes          Yes        Nope          No
## 950           No          Yes          Yes        Nope          No
##     Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 501          No          No          No          No          No
## 950          No         Yes         Yes         Yes          No
##     Q98059.fctr Q98078.fctr Q96024.fctr
## 501         Yes         Yes         Yes
## 950         Yes          No         Yes
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: SKn"
## [1] "max distance(0.9759) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2209    2754          D        SKn           NA           NA           NA
## 5925    1841       <NA>        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2209           No           Pc          Yes           NA           NA
## 5925           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2209           No          Yes          Yes           No          Yes
## 5925           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2209      Science          Yes  Study first           NA           NA
## 5925           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2209           No           NA           NA          Yes           NA
## 5925           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2209           No           Id           No Standard hours           NA
## 5925           NA           NA           NA      Odd hours   Hot headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2209           NA           NA           NA           NA           NA
## 5925          Yes           NA           No          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2209           NA           NA           NA           NA           NA
## 5925           NA         P.M.           No        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2209           NA           NA           NA           NA           NA
## 5925           No           Cs          Yes          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2209           NA           NA           NA           NA           NA
## 5925           No           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2209           NA       People          Yes           NA           NA
## 5925           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2209           NA          Yes           NA          Yes          Mac
## 5925           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2209           NA          Yes            NA         Yes!           NA
## 5925          Yes          Yes Risk-friendly       Umm...           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2209    Socialize           No           NA           NA           NA
## 5925        Space           No    In-person           No           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2209          Yes          Yes           NA           NA           NA
## 5925           NA           NA           Yy          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2209           No          Yes           NA          Yes           NA
## 5925           NA           No           No           No          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2209          Yes           NA           No          Yes           NA
## 5925           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2209         Rent     Optimist          Dad          Yes          Yes
## 5925           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2209          Yes          Yes          Yes      Check!          NA
## 5925           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2209          No         Yes         Yes          No          NA
## 5925          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2209         Yes          NA         Yes
## 5925          NA          NA          NA
## [1] "min distance(0.9611) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4937    6165          D        SKn          Yes          Yes          Yes
## 5572       7       <NA>        SKn          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4937           NA           Pc          Yes          Yes          Yes
## 5572           No           Pc          Yes          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4937           No           No           No          Yes          Yes
## 5572           No          Yes           No          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4937           NA          Yes  Study first           NA          Yes
## 5572      Science          Yes    Try first           No          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4937          Yes    Receiving           NA          Yes          Yes
## 5572          Yes       Giving           No          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4937           No           Pr           No Standard hours  Cool headed
## 5572           No           Id          Yes Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4937           NA           NA          Yes          Yes          Yes
## 5572           No        Happy          Yes           No           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4937          Yes           NA          Yes           NA           No
## 5572          Yes         A.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4937           NA           Cs           No           NA           No
## 5572           No           Me          Yes          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4937           No          Yes          TMI          Yes           No
## 5572          Yes          Yes          TMI          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4937        Tunes       People           No          Yes          Yes
## 5572         Talk       People           No           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4937          Yes          Yes   Supportive           No           PC
## 5572           No          Yes   Supportive           No           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4937           No          Yes     Cautious       Umm...           No
## 5572           No          Yes     Cautious         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4937        Space           No           NA           No           No
## 5572        Space           No       Online           No           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4937          Yes          Yes           Gr           No           No
## 5572          Yes          Yes           Yy           No           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4937           NA           NA           NA           NA           NA
## 5572           No           No           No          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4937           NA           NA           NA           NA           NA
## 5572           No           No           No           No           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4937           NA           NA           NA           NA           NA
## 5572          Own     Optimist          Dad           No           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4937           NA           NA           NA          NA          NA
## 5572          Yes          Yes          Yes        Nope          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4937          NA          NA          NA          NA          NA
## 5572          No          No         Yes          No          No
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4937          NA          NA          NA
## 5572         Yes          No         Yes
## [1] "Category: SKy"
## [1] "max distance(0.9757) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1392    1730          D        SKy           NA           NA           NA
## 1512    1872          D        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           NA          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           No  Study first           No          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1392           NA           NA           NA           NA           NA
## 1512          Yes       Giving           No          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           No           Id          Yes           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           NA          Yes          End          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           No           NA          Yes          Yes           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           No           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1392           NA           NA           NA           NA           NA
## 1512           NA           NA           NA           No           No
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1392           NA           NA           NA          Yes           PC
## 1512           NA          Yes           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 1392          Yes          Yes      Cautious         Yes!           No
## 1512          Yes          Yes Risk-friendly           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1392        Space           No       Online          Yes          Yes
## 1512        Space           No       Online          Yes          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1392          Yes          Yes           Yy           No          Yes
## 1512           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1392          Yes           No          Yes          Yes          Yes
## 1512           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1392          Yes          Yes           No           No           No
## 1512           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1392         Rent     Optimist          Dad           No          Yes
## 1512           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1392          Yes          Yes          Yes      Check!          No
## 1512           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1392         Yes          No         Yes         Yes          No
## 1512          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1392         Yes         Yes          No
## 1512          NA          NA          NA
## [1] "min distance(0.9613) pair:"
##     USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 99      122          D        SKy           No          Yes          Yes
## 769     952          D        SKy           No          Yes          Yes
##     Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 99            No           Pc           No          Yes          Yes
## 769           No           Pc          Yes           No           No
##     Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 99           Yes          Yes          Yes          Yes          Yes
## 769           No          Yes          Yes           No          Yes
##     Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 99       Science           No  Study first           No          Yes
## 769          Art           No  Study first          Yes           No
##     Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 99           Yes       Giving          Yes           No          Yes
## 769          Yes       Giving           No          Yes           No
##     Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 99           Yes           Pr           No Standard hours   Hot headed
## 769          Yes           Pr           No      Odd hours  Cool headed
##     Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 99            No        Happy          Yes          Yes           No
## 769          Yes        Happy           No           No          Yes
##     Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 99           Yes         P.M.          Yes          End          Yes
## 769           No         P.M.          Yes          End          Yes
##     Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 99            No           Me          Yes          Yes          Yes
## 769           No           Cs          Yes           No          Yes
##     Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 99            No           No          TMI          Yes          Yes
## 769           No           No          TMI          Yes          Yes
##     Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 99         Tunes       People          Yes          Yes          Yes
## 769        Tunes   Technology           No          Yes          Yes
##     Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 99            No          Yes    Demanding          Yes           PC
## 769           No          Yes    Demanding          Yes           PC
##     Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 99           Yes          Yes     Cautious       Umm...          Yes
## 769          Yes          Yes     Cautious       Umm...          Yes
##     Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 99         Space           No       Online          Yes           No
## 769        Space           No       Online          Yes           No
##     Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 99           Yes           No           Yy           No          Yes
## 769          Yes          Yes           Gr           No          Yes
##     Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 99            No          Yes          Yes          Yes           No
## 769           No          Yes          Yes          Yes           No
##     Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 99           Yes          Yes          Yes           No           NA
## 769          Yes          Yes          Yes          Yes           No
##     Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 99          Rent           NA           NA           No          Yes
## 769         Rent    Pessimist          Dad           No          Yes
##     Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 99           Yes           NA          Yes      Check!          NA
## 769           No          Yes           No        Nope          No
##     Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 99           NA          NA          No          No          NA
## 769          No         Yes          No          No          No
##     Q98059.fctr Q98078.fctr Q96024.fctr
## 99          Yes         Yes          No
## 769         Yes          No          No
##    Hhold.fctr .clusterid Hhold.fctr.clusterid   D  R  .entropy .knt
## 1           N          1                  N_1  16  3 0.4361623   19
## 2           N          2                  N_2   9  1 0.3250830   10
## 3           N          3                  N_3   8  0 0.0000000    8
## 4           N          4                  N_4   3  1 0.5623351    4
## 5           N          5                  N_5   3  1 0.5623351    4
## 6           N          6                  N_6   1  3 0.5623351    4
## 7         MKn          1                MKn_1  59 11 0.4349016   70
## 8         MKn          2                MKn_2  21 10 0.6287994   31
## 9         MKn          3                MKn_3  17  5 0.5359599   22
## 10        MKy          1                MKy_1  76 16 0.4620369   92
## 11        MKy          2                MKy_2  55 17 0.5465557   72
## 12        MKy          3                MKy_3  55 10 0.4293230   65
## 13        PKn          1                PKn_1  32  3 0.2925085   35
## 14        PKn          2                PKn_2  13  2 0.3926745   15
## 15        PKy          1                PKy_1  10  1 0.3046361   11
## 16        SKn          1                SKn_1 191 56 0.5352834  247
## 17        SKn          2                SKn_2 134 36 0.5162854  170
## 18        SKy          1                SKy_1  22  5 0.4791656   27
## 19        SKy          2                SKy_2   8  1 0.3488321    9
## 20        SKy          3                SKy_3   6  0 0.0000000    6
## 21        SKy          4                SKy_4   3  1 0.5623351    4
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.4839 (98.0037 pct)"
##                     label step_major step_minor label_minor    bgn    end
## 1            cluster.data          1          0           0  9.273 29.707
## 2 partition.data.training          2          0           0 29.707     NA
##   elapsed
## 1  20.434
## 2      NA

Step 2.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
## Loading required package: reshape2
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 3.12 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 3.12 secs"
## [1] "lclgetMatrixSimilarity: duration: 4.624000 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## Stratum 1 
## 
## Population total and number of selected units: 40 7 
## Stratum 2 
## 
## Population total and number of selected units: 97 18 
## Stratum 3 
## 
## Population total and number of selected units: 186 37 
## Stratum 4 
## 
## Population total and number of selected units: 45 7 
## Stratum 5 
## 
## Population total and number of selected units: 10 2 
## Stratum 6 
## 
## Population total and number of selected units: 325 74 
## Stratum 7 
## 
## Population total and number of selected units: 39 7 
## Stratum 8 
## 
## Population total and number of selected units: 9 2 
## Stratum 9 
## 
## Population total and number of selected units: 26 5 
## Stratum 10 
## 
## Population total and number of selected units: 43 10 
## Stratum 11 
## 
## Population total and number of selected units: 5 2 
## Stratum 12 
## 
## Population total and number of selected units: 1 1 
## Stratum 13 
## 
## Population total and number of selected units: 92 19 
## Stratum 14 
## 
## Population total and number of selected units: 7 2 
## Number of strata  14 
## Total number of selected units 193 
## [1] "lclgetMatrixSimilarity: duration: 3.086000 secs"
## [1] "lclgetMatrixSimilarity: duration: 1.288000 secs"
## [1] "lclgetMatrixSimilarity: duration: 1.228000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.624000 secs"

## [1] "Similarity of partitions:"
##         cor cosineSmy obs.x obs.y
## 1 0.9999868 0.9499923   OOB   Fit
## 2 0.9999870 0.9512085   OOB   New
## 3 0.9999873 0.9058551   Fit   New
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 18.30 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA      223
## Fit            590              142       NA
## OOB            152               41       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.8060109        0.1939891       NA
## OOB      0.7875648        0.2124352       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn    324     93    110     0.44262295     0.48186528
## 2        MKy    182     47     55     0.24863388     0.24352332
## 1        MKn    100     23     26     0.13661202     0.11917098
## 4        PKn     41      9     10     0.05601093     0.04663212
## 3          N     40      9     10     0.05464481     0.04663212
## 7        SKy     37      9     10     0.05054645     0.04663212
## 5        PKy      8      3      2     0.01092896     0.01554404
##   .freqRatio.Tst
## 6     0.49327354
## 2     0.24663677
## 1     0.11659193
## 4     0.04484305
## 3     0.04484305
## 7     0.04484305
## 5     0.00896861
## [1] "glbObsAll: "
## [1] 1148  222
## [1] "glbObsTrn: "
## [1] 925 222
## [1] "glbObsFit: "
## [1] 732 221
## [1] "glbObsOOB: "
## [1] 193 221
## [1] "glbObsNew: "
## [1] 223 221
## [1] "partition.data.training chunk: teardown: elapsed: 19.05 secs"
##                     label step_major step_minor label_minor    bgn    end
## 2 partition.data.training          2          0           0 29.707 48.832
## 3         select.features          3          0           0 48.833     NA
##   elapsed
## 2  19.125
## 3      NA

Step 3.0: select features

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
## [1] "cor(Q121699.fctr, Q121700.fctr)=0.7060"
## [1] "cor(Party.fctr, Q121699.fctr)=-0.0619"
## [1] "cor(Party.fctr, Q121700.fctr)=-0.0137"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q121700.fctr as highly correlated with Q121699.fctr
##                         cor.y exclude.as.feat    cor.y.abs   cor.high.X
## YOB              5.385909e-02               1 5.385909e-02         <NA>
## .clusterid       3.185552e-02               1 3.185552e-02         <NA>
## .clusterid.fctr  3.185552e-02               0 3.185552e-02         <NA>
## Gender.fctr      2.915818e-02               0 2.915818e-02         <NA>
## Q108754.fctr     2.873095e-02               0 2.873095e-02         <NA>
## Q108856.fctr     2.598500e-02               0 2.598500e-02         <NA>
## Q120014.fctr     2.540969e-02               0 2.540969e-02         <NA>
## Q115611.fctr     2.414298e-02               0 2.414298e-02         <NA>
## Q102906.fctr     2.383912e-02               0 2.383912e-02         <NA>
## Q101596.fctr     2.350567e-02               0 2.350567e-02         <NA>
## .pos             2.273256e-02               1 2.273256e-02         <NA>
## USER_ID          2.272035e-02               1 2.272035e-02         <NA>
## Q120194.fctr     2.095979e-02               0 2.095979e-02         <NA>
## Hhold.fctr       1.860696e-02               0 1.860696e-02         <NA>
## Q99480.fctr      1.780216e-02               0 1.780216e-02         <NA>
## Q108343.fctr     1.682882e-02               0 1.682882e-02         <NA>
## Q108617.fctr     1.602682e-02               0 1.602682e-02         <NA>
## Q108855.fctr     1.395626e-02               0 1.395626e-02         <NA>
## Q109367.fctr     1.103777e-02               0 1.103777e-02         <NA>
## Q117193.fctr     1.069452e-02               0 1.069452e-02         <NA>
## Q99982.fctr      9.622502e-03               0 9.622502e-03         <NA>
## Q114748.fctr     7.753180e-03               0 7.753180e-03         <NA>
## Q111580.fctr     7.655353e-03               0 7.655353e-03         <NA>
## Q98197.fctr      6.126550e-03               0 6.126550e-03         <NA>
## Q101163.fctr     5.914503e-03               0 5.914503e-03         <NA>
## Q102289.fctr     5.885855e-03               0 5.885855e-03         <NA>
## Q116881.fctr     5.319134e-03               0 5.319134e-03         <NA>
## Q101162.fctr     3.836084e-03               0 3.836084e-03         <NA>
## Q102674.fctr     3.017088e-03               0 3.017088e-03         <NA>
## Q102089.fctr     2.323547e-03               0 2.323547e-03         <NA>
## Q118232.fctr     2.165304e-03               0 2.165304e-03         <NA>
## Q118117.fctr     6.721935e-04               0 6.721935e-04         <NA>
## Q99581.fctr      1.641131e-05               0 1.641131e-05         <NA>
## Q108342.fctr    -1.165784e-04               0 1.165784e-04         <NA>
## Q113584.fctr    -2.536542e-04               0 2.536542e-04         <NA>
## Edn.fctr        -4.631990e-04               0 4.631990e-04         <NA>
## Q113181.fctr    -2.987810e-03               0 2.987810e-03         <NA>
## Q115899.fctr    -3.391939e-03               0 3.391939e-03         <NA>
## Q122771.fctr    -3.593507e-03               0 3.593507e-03         <NA>
## Q106388.fctr    -4.089574e-03               0 4.089574e-03         <NA>
## Q113583.fctr    -5.119777e-03               0 5.119777e-03         <NA>
## Q119334.fctr    -5.832642e-03               0 5.832642e-03         <NA>
## Q105655.fctr    -5.967822e-03               0 5.967822e-03         <NA>
## Q115777.fctr    -6.671644e-03               0 6.671644e-03         <NA>
## Q98869.fctr     -7.300228e-03               0 7.300228e-03         <NA>
## Q115602.fctr    -8.572164e-03               0 8.572164e-03         <NA>
## Q107869.fctr    -8.710476e-03               0 8.710476e-03         <NA>
## .rnorm          -1.022887e-02               0 1.022887e-02         <NA>
## Q120472.fctr    -1.117239e-02               0 1.117239e-02         <NA>
## Q100562.fctr    -1.304301e-02               0 1.304301e-02         <NA>
## Q115610.fctr    -1.308619e-02               0 1.308619e-02         <NA>
## Q121700.fctr    -1.373009e-02               0 1.373009e-02 Q121699.fctr
## Q106042.fctr    -1.533181e-02               0 1.533181e-02         <NA>
## Q116441.fctr    -1.548317e-02               0 1.548317e-02         <NA>
## YOB.Age.dff     -1.579002e-02               0 1.579002e-02         <NA>
## Q119650.fctr    -1.650551e-02               0 1.650551e-02         <NA>
## Q120978.fctr    -1.708157e-02               0 1.708157e-02         <NA>
## Income.fctr     -1.805407e-02               0 1.805407e-02         <NA>
## Q99716.fctr     -1.819015e-02               0 1.819015e-02         <NA>
## Q102687.fctr    -1.939956e-02               0 1.939956e-02         <NA>
## Q107491.fctr    -1.953297e-02               0 1.953297e-02         <NA>
## Q100010.fctr    -2.019365e-02               0 2.019365e-02         <NA>
## Q112270.fctr    -2.229876e-02               0 2.229876e-02         <NA>
## Q123464.fctr    -2.303014e-02               0 2.303014e-02         <NA>
## Q104996.fctr    -2.394016e-02               0 2.394016e-02         <NA>
## Q116797.fctr    -2.401541e-02               0 2.401541e-02         <NA>
## Q116601.fctr    -2.424136e-02               0 2.424136e-02         <NA>
## Q116953.fctr    -2.434193e-02               0 2.434193e-02         <NA>
## Q110740.fctr    -2.599180e-02               0 2.599180e-02         <NA>
## Q103293.fctr    -2.609312e-02               0 2.609312e-02         <NA>
## Q122120.fctr    -2.635370e-02               0 2.635370e-02         <NA>
## Q108950.fctr    -2.796314e-02               0 2.796314e-02         <NA>
## Q100680.fctr    -2.839756e-02               0 2.839756e-02         <NA>
## Q122769.fctr    -2.867161e-02               0 2.867161e-02         <NA>
## Q106993.fctr    -2.914206e-02               0 2.914206e-02         <NA>
## Q111848.fctr    -3.061679e-02               0 3.061679e-02         <NA>
## Q121011.fctr    -3.123022e-02               0 3.123022e-02         <NA>
## Q115195.fctr    -3.162016e-02               0 3.162016e-02         <NA>
## Q120650.fctr    -3.168805e-02               0 3.168805e-02         <NA>
## Q96024.fctr     -3.182566e-02               0 3.182566e-02         <NA>
## Q112512.fctr    -3.209601e-02               0 3.209601e-02         <NA>
## Q118233.fctr    -3.272307e-02               0 3.272307e-02         <NA>
## Q116448.fctr    -3.325689e-02               0 3.325689e-02         <NA>
## Q106389.fctr    -3.366341e-02               0 3.366341e-02         <NA>
## Q118237.fctr    -3.406424e-02               0 3.406424e-02         <NA>
## Q124742.fctr    -3.410578e-02               0 3.410578e-02         <NA>
## Q111220.fctr    -3.435982e-02               0 3.435982e-02         <NA>
## Q117186.fctr    -3.488234e-02               0 3.488234e-02         <NA>
## Q106272.fctr    -3.593469e-02               0 3.593469e-02         <NA>
## Q98059.fctr     -3.688543e-02               0 3.688543e-02         <NA>
## Q120379.fctr    -3.777954e-02               0 3.777954e-02         <NA>
## Q105840.fctr    -3.782042e-02               0 3.782042e-02         <NA>
## Q114961.fctr    -3.846716e-02               0 3.846716e-02         <NA>
## Q98578.fctr     -3.905193e-02               0 3.905193e-02         <NA>
## Q122770.fctr    -3.924562e-02               0 3.924562e-02         <NA>
## Q106997.fctr    -3.925127e-02               0 3.925127e-02         <NA>
## YOB.Age.fctr    -4.299538e-02               0 4.299538e-02         <NA>
## Q98078.fctr     -4.318029e-02               0 4.318029e-02         <NA>
## Q100689.fctr    -4.610720e-02               0 4.610720e-02         <NA>
## Q119851.fctr    -4.784957e-02               0 4.784957e-02         <NA>
## Q112478.fctr    -4.791303e-02               0 4.791303e-02         <NA>
## Q118892.fctr    -4.849461e-02               0 4.849461e-02         <NA>
## Q116197.fctr    -4.953483e-02               0 4.953483e-02         <NA>
## Q113992.fctr    -5.060894e-02               0 5.060894e-02         <NA>
## Q123621.fctr    -5.140758e-02               0 5.140758e-02         <NA>
## Q120012.fctr    -5.301143e-02               0 5.301143e-02         <NA>
## Q115390.fctr    -5.425424e-02               0 5.425424e-02         <NA>
## Q121699.fctr    -6.186040e-02               0 6.186040e-02         <NA>
## Q114517.fctr    -6.233932e-02               0 6.233932e-02         <NA>
## Q124122.fctr    -6.976947e-02               0 6.976947e-02         <NA>
## Q114386.fctr    -7.613008e-02               0 7.613008e-02         <NA>
## Q114152.fctr    -7.783674e-02               0 7.783674e-02         <NA>
## Q109244.fctr               NA               0           NA         <NA>
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## YOB              1.055556     6.8108108   FALSE FALSE            FALSE
## .clusterid       1.631922     0.6486486   FALSE FALSE            FALSE
## .clusterid.fctr  1.631922     0.6486486   FALSE FALSE            FALSE
## Gender.fctr      1.531680     0.3243243   FALSE FALSE            FALSE
## Q108754.fctr     1.830986     0.3243243   FALSE FALSE            FALSE
## Q108856.fctr     2.574074     0.3243243   FALSE FALSE            FALSE
## Q120014.fctr     1.266447     0.3243243   FALSE FALSE            FALSE
## Q115611.fctr     3.698795     0.3243243   FALSE FALSE            FALSE
## Q102906.fctr     1.571429     0.3243243   FALSE FALSE            FALSE
## Q101596.fctr     2.316514     0.3243243   FALSE FALSE            FALSE
## .pos             1.000000   100.0000000   FALSE FALSE            FALSE
## USER_ID          1.000000   100.0000000   FALSE FALSE            FALSE
## Q120194.fctr     1.694118     0.3243243   FALSE FALSE            FALSE
## Hhold.fctr       1.820961     0.7567568   FALSE FALSE            FALSE
## Q99480.fctr      2.540284     0.3243243   FALSE FALSE            FALSE
## Q108343.fctr     1.331361     0.3243243   FALSE FALSE            FALSE
## Q108617.fctr     5.685950     0.3243243   FALSE FALSE            FALSE
## Q108855.fctr     1.422713     0.3243243   FALSE FALSE            FALSE
## Q109367.fctr     1.912458     0.3243243   FALSE FALSE            FALSE
## Q117193.fctr     1.404844     0.3243243   FALSE FALSE            FALSE
## Q99982.fctr      1.519298     0.3243243   FALSE FALSE             TRUE
## Q114748.fctr     1.256716     0.3243243   FALSE FALSE             TRUE
## Q111580.fctr     1.646429     0.3243243   FALSE FALSE             TRUE
## Q98197.fctr      2.581731     0.3243243   FALSE FALSE             TRUE
## Q101163.fctr     1.098802     0.3243243   FALSE FALSE             TRUE
## Q102289.fctr     2.734694     0.3243243   FALSE FALSE             TRUE
## Q116881.fctr     2.200873     0.3243243   FALSE FALSE             TRUE
## Q101162.fctr     1.716912     0.3243243   FALSE FALSE             TRUE
## Q102674.fctr     1.614286     0.3243243   FALSE FALSE             TRUE
## Q102089.fctr     1.779851     0.3243243   FALSE FALSE             TRUE
## Q118232.fctr     1.185065     0.3243243   FALSE FALSE             TRUE
## Q118117.fctr     1.736059     0.3243243   FALSE FALSE             TRUE
## Q99581.fctr      3.551913     0.3243243   FALSE FALSE             TRUE
## Q108342.fctr     1.656667     0.3243243   FALSE FALSE             TRUE
## Q113584.fctr     1.008380     0.3243243   FALSE FALSE             TRUE
## Edn.fctr         1.519737     0.8648649   FALSE FALSE             TRUE
## Q113181.fctr     2.378995     0.3243243   FALSE FALSE             TRUE
## Q115899.fctr     1.135542     0.3243243   FALSE FALSE             TRUE
## Q122771.fctr     2.936275     0.3243243   FALSE FALSE             TRUE
## Q106388.fctr     2.675000     0.3243243   FALSE FALSE             TRUE
## Q113583.fctr     2.387850     0.3243243   FALSE FALSE             TRUE
## Q119334.fctr     1.069767     0.3243243   FALSE FALSE             TRUE
## Q105655.fctr     1.210826     0.3243243   FALSE FALSE             TRUE
## Q115777.fctr     1.436426     0.3243243   FALSE FALSE             TRUE
## Q98869.fctr      2.187500     0.3243243   FALSE FALSE             TRUE
## Q115602.fctr     2.958974     0.3243243   FALSE FALSE             TRUE
## Q107869.fctr     1.021220     0.3243243   FALSE FALSE             TRUE
## .rnorm           1.000000   100.0000000   FALSE FALSE            FALSE
## Q120472.fctr     1.631579     0.3243243   FALSE FALSE            FALSE
## Q100562.fctr     3.216931     0.3243243   FALSE FALSE            FALSE
## Q115610.fctr     3.240642     0.3243243   FALSE FALSE            FALSE
## Q121700.fctr     3.472826     0.3243243   FALSE FALSE            FALSE
## Q106042.fctr     1.235650     0.3243243   FALSE FALSE            FALSE
## Q116441.fctr     2.041667     0.3243243   FALSE FALSE            FALSE
## YOB.Age.dff      1.027586     1.7297297   FALSE FALSE            FALSE
## Q119650.fctr     2.458150     0.3243243   FALSE FALSE            FALSE
## Q120978.fctr     1.331148     0.3243243   FALSE FALSE            FALSE
## Income.fctr      1.056738     0.7567568   FALSE FALSE            FALSE
## Q99716.fctr      3.418478     0.3243243   FALSE FALSE            FALSE
## Q102687.fctr     1.125698     0.3243243   FALSE FALSE            FALSE
## Q107491.fctr     4.744966     0.3243243   FALSE FALSE            FALSE
## Q100010.fctr     3.204420     0.3243243   FALSE FALSE            FALSE
## Q112270.fctr     1.675373     0.3243243   FALSE FALSE            FALSE
## Q123464.fctr     2.484127     0.3243243   FALSE FALSE            FALSE
## Q104996.fctr     1.071038     0.3243243   FALSE FALSE            FALSE
## Q116797.fctr     1.715356     0.3243243   FALSE FALSE            FALSE
## Q116601.fctr     3.411765     0.3243243   FALSE FALSE            FALSE
## Q116953.fctr     2.000000     0.3243243   FALSE FALSE            FALSE
## Q110740.fctr     1.245665     0.3243243   FALSE FALSE            FALSE
## Q103293.fctr     1.102778     0.3243243   FALSE FALSE            FALSE
## Q122120.fctr     2.651961     0.3243243   FALSE FALSE            FALSE
## Q108950.fctr     2.028470     0.3243243   FALSE FALSE            FALSE
## Q100680.fctr     2.336323     0.3243243   FALSE FALSE            FALSE
## Q122769.fctr     1.627376     0.3243243   FALSE FALSE            FALSE
## Q106993.fctr     3.842767     0.3243243   FALSE FALSE            FALSE
## Q111848.fctr     2.304721     0.3243243   FALSE FALSE            FALSE
## Q121011.fctr     1.541958     0.3243243   FALSE FALSE            FALSE
## Q115195.fctr     2.173913     0.3243243   FALSE FALSE            FALSE
## Q120650.fctr     3.084507     0.3243243   FALSE FALSE            FALSE
## Q96024.fctr      1.284810     0.3243243   FALSE FALSE            FALSE
## Q112512.fctr     3.346154     0.3243243   FALSE FALSE            FALSE
## Q118233.fctr     2.375000     0.3243243   FALSE FALSE            FALSE
## Q116448.fctr     1.019337     0.3243243   FALSE FALSE            FALSE
## Q106389.fctr     1.122807     0.3243243   FALSE FALSE            FALSE
## Q118237.fctr     1.005682     0.3243243   FALSE FALSE            FALSE
## Q124742.fctr     1.712230     0.3243243   FALSE FALSE            FALSE
## Q111220.fctr     2.500000     0.3243243   FALSE FALSE            FALSE
## Q117186.fctr     1.983051     0.3243243   FALSE FALSE            FALSE
## Q106272.fctr     1.921875     0.3243243   FALSE FALSE            FALSE
## Q98059.fctr      4.217391     0.3243243   FALSE FALSE            FALSE
## Q120379.fctr     1.279743     0.3243243   FALSE FALSE            FALSE
## Q105840.fctr     1.295031     0.3243243   FALSE FALSE            FALSE
## Q114961.fctr     1.095930     0.3243243   FALSE FALSE            FALSE
## Q98578.fctr      1.853175     0.3243243   FALSE FALSE            FALSE
## Q122770.fctr     1.355049     0.3243243   FALSE FALSE            FALSE
## Q106997.fctr     1.095109     0.3243243   FALSE FALSE            FALSE
## YOB.Age.fctr     1.100719     0.9729730   FALSE FALSE            FALSE
## Q98078.fctr      1.051576     0.3243243   FALSE FALSE            FALSE
## Q100689.fctr     1.865672     0.3243243   FALSE FALSE            FALSE
## Q119851.fctr     1.059322     0.3243243   FALSE FALSE            FALSE
## Q112478.fctr     1.733083     0.3243243   FALSE FALSE            FALSE
## Q118892.fctr     2.668317     0.3243243   FALSE FALSE            FALSE
## Q116197.fctr     1.708333     0.3243243   FALSE FALSE            FALSE
## Q113992.fctr     2.575472     0.3243243   FALSE FALSE            FALSE
## Q123621.fctr     1.002985     0.3243243   FALSE FALSE            FALSE
## Q120012.fctr     1.019943     0.3243243   FALSE FALSE            FALSE
## Q115390.fctr     1.543478     0.3243243   FALSE FALSE            FALSE
## Q121699.fctr     3.090909     0.3243243   FALSE FALSE            FALSE
## Q114517.fctr     2.334821     0.3243243   FALSE FALSE            FALSE
## Q124122.fctr     1.375912     0.3243243   FALSE FALSE            FALSE
## Q114386.fctr     1.211310     0.3243243   FALSE FALSE            FALSE
## Q114152.fctr     2.319444     0.3243243   FALSE FALSE            FALSE
## Q109244.fctr     0.000000     0.1081081    TRUE  TRUE               NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

##              cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr    NA               0        NA       <NA>         0
##              percentUnique zeroVar  nzv is.cor.y.abs.low
## Q109244.fctr     0.1081081    TRUE TRUE               NA
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##         48        223 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##          49 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##               9             163              59             136 
##           Party         Q124742         Q124122         Q123464 
##              NA             589             337             309 
##         Q123621         Q122769         Q122770         Q122771 
##             312             279             245             247 
##         Q122120         Q121699         Q121700         Q120978 
##             248             224             222             257 
##         Q121011         Q120379         Q120650         Q120472 
##             238             269             261             280 
##         Q120194         Q120012         Q120014         Q119334 
##             292             265             289             257 
##         Q119851         Q119650         Q118892         Q118117 
##             245             274             227             228 
##         Q118232         Q118233         Q118237         Q117186 
##             311             268             264             283 
##         Q117193         Q116797         Q116881         Q116953 
##             276             242             276             279 
##         Q116601         Q116441         Q116448         Q116197 
##             225             239             243             248 
##         Q115602         Q115777         Q115610         Q115611 
##             236             264             230             202 
##         Q115899         Q115390         Q114961         Q114748 
##             260             279             244             207 
##         Q115195         Q114517         Q114386         Q113992 
##             242             216             214             206 
##         Q114152         Q113583         Q113584         Q113181 
##             261             235             241             217 
##         Q112478         Q112512         Q112270         Q111848 
##             229             212             249             179 
##         Q111580         Q111220         Q110740         Q109367 
##             219             191             173              73 
##         Q108950         Q109244         Q108855         Q108617 
##              91               0             191             143 
##         Q108856         Q108754         Q108342         Q108343 
##             183             148             157             162 
##         Q107869         Q107491         Q106993         Q106997 
##             196             176             187             193 
##         Q106272         Q106388         Q106389         Q106042 
##             212             228             236             218 
##         Q105840         Q105655         Q104996         Q103293 
##             222             177             201             201 
##         Q102906         Q102674         Q102687         Q102289 
##             231             237             199             227 
##         Q102089         Q101162         Q101163         Q101596 
##             222             224             271             243 
##         Q100689         Q100680         Q100562          Q99982 
##             185             216             226             243 
##         Q100010          Q99716          Q99581          Q99480 
##             210             219             214             206 
##          Q98869          Q98578          Q98059          Q98078 
##             254             252             195             251 
##          Q98197          Q96024            .lcn 
##             224             243             223
## [1] "glb_feats_df:"
## [1] 113  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id      cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID 0.02272035            TRUE 0.02272035       <NA>
## Party.fctr Party.fctr         NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##             label step_major step_minor label_minor    bgn    end elapsed
## 3 select.features          3          0           0 48.833 51.324   2.491
## 4      fit.models          4          0           0 51.324     NA      NA

Step 4.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 51.853  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor    bgn    end
## 1 fit.models_0_bgn          1          0         setup 51.853 51.886
## 2 fit.models_0_MFO          1          1 myMFO_classfr 51.886     NA
##   elapsed
## 1   0.033
## 2      NA
## [1] "myfit_mdl: enter: 0.002000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.454000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
##         D         R 
## 0.8060109 0.1939891 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.890000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.894000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 5       0.20       0 0.8060109       0
## 6       0.25       0 0.8060109       0
## 7       0.30       0 0.8060109       0
## 8       0.35       0 0.8060109       0
## 9       0.40       0 0.8060109       0
## 10      0.45       0 0.8060109       0
## 11      0.50       0 0.8060109       0
## 12      0.55       0 0.8060109       0
## 13      0.60       0 0.8060109       0
## 14      0.65       0 0.8060109       0
## 15      0.70       0 0.8060109       0
## 16      0.75       0 0.8060109       0
## 17      0.80       0 0.8060109       0
## 18      0.85       0 0.8060109       0
## 19      0.90       0 0.8060109       0
## 20      0.95       0 0.8060109       0
## 21      1.00       0 0.8060109       0

##          Prediction
## Reference   D   R
##         D 590   0
##         R 142   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.060109e-01   0.000000e+00   7.754723e-01   8.340591e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   5.224287e-01   2.652612e-32 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 5       0.20       0 0.7875648       0
## 6       0.25       0 0.7875648       0
## 7       0.30       0 0.7875648       0
## 8       0.35       0 0.7875648       0
## 9       0.40       0 0.7875648       0
## 10      0.45       0 0.7875648       0
## 11      0.50       0 0.7875648       0
## 12      0.55       0 0.7875648       0
## 13      0.60       0 0.7875648       0
## 14      0.65       0 0.7875648       0
## 15      0.70       0 0.7875648       0
## 16      0.75       0 0.7875648       0
## 17      0.80       0 0.7875648       0
## 18      0.85       0 0.7875648       0
## 19      0.90       0 0.7875648       0
## 20      0.95       0 0.7875648       0
## 21      1.00       0 0.7875648       0
##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 7.606000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.429
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            1            0
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5               0        0.8060109
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7754723             0.8340591             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            1            0             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5               0        0.7875648
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7230441             0.8430301             0
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.8060109 0.1939891
## 2 0.8060109 0.1939891
## 3 0.8060109 0.1939891
## 4 0.8060109 0.1939891
## 5 0.8060109 0.1939891
## 6 0.8060109 0.1939891
## [1] "myfit_mdl: exit: 7.649000 secs"
##                 label step_major step_minor      label_minor    bgn   end
## 2    fit.models_0_MFO          1          1    myMFO_classfr 51.886 59.54
## 3 fit.models_0_Random          1          2 myrandom_classfr 59.541    NA
##   elapsed
## 2   7.654
## 3      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.404000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.675000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used

## [1] "myfit_mdl: train diagnostics complete: 0.677000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D 590   0
##         R 142   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.060109e-01   0.000000e+00   7.754723e-01   8.340591e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   5.224287e-01   2.652612e-32 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 7.301000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.267                 0.001       0.5244092
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.8305085    0.2183099       0.4867868                   0.85
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1               0        0.8060109             0.7754723
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8340591             0       0.4922978    0.7894737
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.195122       0.5012035                   0.85               0
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7875648             0.7230441             0.8430301
##   max.Kappa.OOB
## 1             0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.532000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##      bgn    end elapsed
## 3 59.541 67.085   7.544
## 4 67.086     NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
## 
##     expand
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.000641 on full training set
## [1] "myfit_mdl: train complete: 1.491000 secs"
##   alpha       lambda
## 1   0.1 0.0006408924

##             Length Class      Mode     
## a0           48    -none-     numeric  
## beta        192    dgCMatrix  S4       
## df           48    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       48    -none-     numeric  
## dev.ratio    48    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##            (Intercept)         Q114152.fctrNo        Q114152.fctrYes 
##            -1.13166150             0.01441013            -0.47004128 
## Q114386.fctrMysterious        Q114386.fctrTMI 
##            -0.30122736            -0.23102023 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"            "Q114152.fctrNo"        
## [3] "Q114152.fctrYes"        "Q114386.fctrMysterious"
## [5] "Q114386.fctrTMI"       
## [1] "myfit_mdl: train diagnostics complete: 1.598000 secs"

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 6       0.25       0 0.8060109       0
## 7       0.30       0 0.8060109       0
## 8       0.35       0 0.8060109       0
## 9       0.40       0 0.8060109       0
## 10      0.45       0 0.8060109       0
## 11      0.50       0 0.8060109       0
## 12      0.55       0 0.8060109       0
## 13      0.60       0 0.8060109       0
## 14      0.65       0 0.8060109       0
## 15      0.70       0 0.8060109       0
## 16      0.75       0 0.8060109       0
## 17      0.80       0 0.8060109       0
## 18      0.85       0 0.8060109       0
## 19      0.90       0 0.8060109       0
## 20      0.95       0 0.8060109       0
## 21      1.00       0 0.8060109       0

##          Prediction
## Reference   D   R
##         D 590   0
##         R 142   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.060109e-01   0.000000e+00   7.754723e-01   8.340591e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   5.224287e-01   2.652612e-32

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 6       0.25       0 0.7875648       0
## 7       0.30       0 0.7875648       0
## 8       0.35       0 0.7875648       0
## 9       0.40       0 0.7875648       0
## 10      0.45       0 0.7875648       0
## 11      0.50       0 0.7875648       0
## 12      0.55       0 0.7875648       0
## 13      0.60       0 0.7875648       0
## 14      0.65       0 0.7875648       0
## 15      0.70       0 0.7875648       0
## 16      0.75       0 0.7875648       0
## 17      0.80       0 0.7875648       0
## 18      0.85       0 0.7875648       0
## 19      0.90       0 0.7875648       0
## 20      0.95       0 0.7875648       0
## 21      1.00       0 0.7875648       0

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 8.050000 secs"
##                           id                     feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q114152.fctr,Q114386.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.788                 0.015             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0       0.5625269                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1               0        0.8060109             0.7754723
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8340591             0             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0       0.5478979                    0.5               0
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7875648             0.7230441             0.8430301
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 8.098000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.blockParallel = glbMdlSequential,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q114152.fctr,Q114386.fctr"
## [1] "myfit_mdl: setup complete: 0.696000 secs"
## Loading required package: rpart
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.276000 secs"
##   cp
## 1  0
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 732 
## 
##   CP nsplit rel error
## 1  0      0         1
## 
## Node number 1: 732 observations
##   predicted class=D  expected loss=0.1939891  P(node) =1
##     class counts:   590   142
##    probabilities: 0.806 0.194 
## 
## n= 732 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 732 142 D (0.8060109 0.1939891) *
## [1] "myfit_mdl: train diagnostics complete: 2.429000 secs"
## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 5       0.20       0 0.8060109       0
## 6       0.25       0 0.8060109       0
## 7       0.30       0 0.8060109       0
## 8       0.35       0 0.8060109       0
## 9       0.40       0 0.8060109       0
## 10      0.45       0 0.8060109       0
## 11      0.50       0 0.8060109       0
## 12      0.55       0 0.8060109       0
## 13      0.60       0 0.8060109       0
## 14      0.65       0 0.8060109       0
## 15      0.70       0 0.8060109       0
## 16      0.75       0 0.8060109       0
## 17      0.80       0 0.8060109       0
## 18      0.85       0 0.8060109       0
## 19      0.90       0 0.8060109       0
## 20      0.95       0 0.8060109       0
## 21      1.00       0 0.8060109       0

##          Prediction
## Reference   D   R
##         D 590   0
##         R 142   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.060109e-01   0.000000e+00   7.754723e-01   8.340591e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   5.224287e-01   2.652612e-32 
## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 5       0.20       0 0.7875648       0
## 6       0.25       0 0.7875648       0
## 7       0.30       0 0.7875648       0
## 8       0.35       0 0.7875648       0
## 9       0.40       0 0.7875648       0
## 10      0.45       0 0.7875648       0
## 11      0.50       0 0.7875648       0
## 12      0.55       0 0.7875648       0
## 13      0.60       0 0.7875648       0
## 14      0.65       0 0.7875648       0
## 15      0.70       0 0.7875648       0
## 16      0.75       0 0.7875648       0
## 17      0.80       0 0.7875648       0
## 18      0.85       0 0.7875648       0
## 19      0.90       0 0.7875648       0
## 20      0.95       0 0.7875648       0
## 21      1.00       0 0.7875648       0

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 8.951000 secs"
##                     id                     feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q114152.fctr,Q114386.fctr               1
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.576                 0.008             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0             0.5                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1               0        0.8060121             0.7754723
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8340591             0             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0             0.5                    0.5               0
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7875648             0.7230441             0.8430301
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0        0.001793698               0
## [1] "myfit_mdl: exit: 8.995000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.blockParallel = glbMdlSequential,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.blockParallel = glbMdlSequential,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.blockParallel = glbMdlSequential,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.blockParallel = glbMdlSequential,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.blockParallel = glbMdlSequential,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##      bgn    end elapsed
## 4 67.086 84.218  17.132
## 5 84.219     NA      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr"
## [1] "myfit_mdl: setup complete: 0.704000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0211 on full training set
## [1] "myfit_mdl: train complete: 2.433000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0           56    -none-     numeric  
## beta        560    dgCMatrix  S4       
## df           56    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       56    -none-     numeric  
## dev.ratio    56    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames       10    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)   Q114152.fctrNA:Q121699.fctrNo 
##                      -1.3660730                       0.8520755 
##  Q114152.fctrNA:Q121699.fctrYes Q114152.fctrYes:Q121699.fctrYes 
##                      -0.1969547                      -0.6620578 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)   Q114152.fctrNA:Q121699.fctrNo 
##                    -1.362457755                     0.874180212 
##  Q114152.fctrYes:Q121699.fctrNo  Q114152.fctrNA:Q121699.fctrYes 
##                     0.009467681                    -0.225224906 
## Q114152.fctrYes:Q121699.fctrYes 
##                    -0.693412776 
## [1] "myfit_mdl: train diagnostics complete: 3.041000 secs"

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 9       0.40       0 0.8060109       0
## 10      0.45       0 0.8060109       0
## 11      0.50       0 0.8060109       0
## 12      0.55       0 0.8060109       0
## 13      0.60       0 0.8060109       0
## 14      0.65       0 0.8060109       0
## 15      0.70       0 0.8060109       0
## 16      0.75       0 0.8060109       0
## 17      0.80       0 0.8060109       0
## 18      0.85       0 0.8060109       0
## 19      0.90       0 0.8060109       0
## 20      0.95       0 0.8060109       0
## 21      1.00       0 0.8060109       0

##          Prediction
## Reference   D   R
##         D 590   0
##         R 142   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.060109e-01   0.000000e+00   7.754723e-01   8.340591e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   5.224287e-01   2.652612e-32

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold f.score  accuracy g.score
## 9       0.40       0 0.7875648       0
## 10      0.45       0 0.7875648       0
## 11      0.50       0 0.7875648       0
## 12      0.55       0 0.7875648       0
## 13      0.60       0 0.7875648       0
## 14      0.65       0 0.7875648       0
## 15      0.70       0 0.7875648       0
## 16      0.75       0 0.7875648       0
## 17      0.80       0 0.7875648       0
## 18      0.85       0 0.7875648       0
## 19      0.90       0 0.7875648       0
## 20      0.95       0 0.7875648       0
## 21      1.00       0 0.7875648       0

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 9.529000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                 feats max.nTuningRuns
## 1 Q114152.fctr,Q114386.fctr,Q114152.fctr:Q121699.fctr              20
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.724                 0.017             0.5
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1            1            0       0.5972487                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1               0        0.8060121             0.7754723
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.8340591             0             0.5            1
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1            0       0.4963094                    0.5               0
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.7875648             0.7230441             0.8430301
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0        0.001881246               0
## [1] "myfit_mdl: exit: 9.599000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##      bgn   end elapsed
## 5 84.219 93.83   9.611
## 6 93.830    NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.blockParallel = glbMdlSequential,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.739000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0223 on full training set
## [1] "myfit_mdl: train complete: 27.659000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             84  -none-     numeric  
## beta        22848  dgCMatrix  S4       
## df             84  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         84  -none-     numeric  
## dev.ratio      84  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        272  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                    -1.291984516                    -0.359967281 
##                      Edn.fctr^5                   Hhold.fctrPKn 
##                     0.229127626                    -0.048297383 
##                  Q100562.fctrNo                  Q100680.fctrNo 
##                     0.103164723                     0.011944055 
##                  Q101596.fctrNo                  Q112478.fctrNo 
##                     0.014216556                     0.123652868 
##                 Q114152.fctrYes                  Q115610.fctrNo 
##                    -0.154057660                     0.156094317 
##                Q116197.fctrA.M.                  Q116953.fctrNo 
##                     0.093159631                     0.081367555 
##      Q117193.fctrStandard hours                  Q118232.fctrId 
##                     0.008141328                    -0.261912459 
##                  Q119851.fctrNo         Q120194.fctrStudy first 
##                     0.012983392                    -0.138311662 
##           Q120194.fctrTry first                   Q98197.fctrNo 
##                     0.133738104                    -0.308007491 
##                  Q98578.fctrYes    Hhold.fctrN:.clusterid.fctr6 
##                    -0.061503513                     1.919850547 
## YOB.Age.fctr(15,20]:YOB.Age.dff 
##                     0.059071038 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                    -1.320911682                    -0.421891827 
##                      Edn.fctr^5                   Hhold.fctrPKn 
##                     0.236006615                    -0.071954796 
##                  Q100562.fctrNo                  Q100680.fctrNo 
##                     0.147431816                     0.038869951 
##                  Q101596.fctrNo                  Q106042.fctrNo 
##                     0.047949711                     0.004979051 
##                  Q106272.fctrNo                  Q112478.fctrNo 
##                     0.011597674                     0.153223876 
##                 Q114152.fctrYes                  Q114517.fctrNo 
##                    -0.189846153                     0.015395017 
##                 Q114517.fctrYes                  Q115390.fctrNo 
##                    -0.017066286                     0.026580076 
##                  Q115610.fctrNo                Q116197.fctrA.M. 
##                     0.193618993                     0.131674135 
##                  Q116953.fctrNo         Q117186.fctrCool headed 
##                     0.106235592                     0.011479302 
##      Q117193.fctrStandard hours                  Q118232.fctrId 
##                     0.030159368                    -0.306219222 
##                  Q119851.fctrNo         Q120194.fctrStudy first 
##                     0.044376260                    -0.181328326 
##           Q120194.fctrTry first                  Q124742.fctrNo 
##                     0.128292688                    -0.024955507 
##                   Q98197.fctrNo                  Q98578.fctrYes 
##                    -0.368031159                    -0.101027779 
##                  YOB.Age.fctr^7  Hhold.fctrMKn:.clusterid.fctr2 
##                     0.043516626                     0.057488010 
##    Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff 
##                     2.075389191                     0.070473734 
## [1] "myfit_mdl: train diagnostics complete: 28.305000 secs"

##          Prediction
## Reference   D   R
##         D 589   1
##         R 132  10
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.183060e-01   1.057756e-01   7.884255e-01   8.455940e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   2.144711e-01   1.795460e-29

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 37.529000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     26.852                 9.473
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5105634            1   0.02112676       0.7311112
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.3        0.130719        0.8069191
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7884255              0.845594    0.01298693
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.4967105    0.9934211            0        0.546534
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.65               0        0.7875648
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7230441             0.8430301             0
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.003930609      0.02570105
## [1] "myfit_mdl: exit: 37.731000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet  93.830 131.592
## 7       fit.models_0_end          1          6    teardown 131.593      NA
##   elapsed
## 6  37.763
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##        label step_major step_minor label_minor     bgn     end elapsed
## 4 fit.models          4          0           0  51.324 131.606  80.282
## 5 fit.models          4          1           1 131.607      NA      NA
if (!is.null(glbChunks$first) && (glbChunks$first == "fit.models_1") &&
    (is.null(knitr::opts_current$get(name = 'label')))) # not knitting
    myloadChunk(glbChunks$inpFilePathName, 
                keepSpec = c("glbMdlFamilies","glbMdlSequential","glbMdlPreprocMethods",
                             "glbMdlTuneParams","glbMdlSelId","glbMdlEnsemble"), 
                dropSpec = c(NULL))
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 136.172  NA      NA
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 136.172 136.185
## 2 fit.models_1_All.X          1          1       setup 136.186      NA
##   elapsed
## 1   0.014
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 136.186 136.193
## 3 fit.models_1_All.X          1          2      glmnet 136.194      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 1.029000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.04 on full training set
## [1] "myfit_mdl: train complete: 23.583000 secs"

##             Length Class      Mode     
## a0            100  -none-     numeric  
## beta        27400  dgCMatrix  S4       
## df            100  -none-     numeric  
## dim             2  -none-     numeric  
## lambda        100  -none-     numeric  
## dev.ratio     100  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        274  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                    -1.285449323                    -0.304102172 
##                      Edn.fctr^5                   Hhold.fctrPKn 
##                     0.214607283                    -0.055097266 
##                  Q100562.fctrNo                  Q100680.fctrNo 
##                     0.085446768                     0.002204116 
##                  Q101596.fctrNo                  Q112478.fctrNo 
##                     0.002890755                     0.105009913 
##                 Q114152.fctrYes                  Q115610.fctrNo 
##                    -0.133752090                     0.130323349 
##                Q116197.fctrA.M.                  Q116953.fctrNo 
##                     0.073139264                     0.072668101 
##      Q117193.fctrStandard hours                  Q118232.fctrId 
##                     0.010279080                    -0.224580754 
##                  Q119851.fctrNo         Q120194.fctrStudy first 
##                     0.008937906                    -0.116072075 
##           Q120194.fctrTry first                  Q121699.fctrNo 
##                     0.136945422                     0.081298033 
##                 Q121699.fctrYes                   Q98197.fctrNo 
##                    -0.060852588                    -0.250727021 
##                  Q98578.fctrYes                  YOB.Age.fctr^7 
##                    -0.063253634                     0.001048372 
##    Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff 
##                     1.773160941                     0.040871999 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                   -1.3026929694                   -0.3600681029 
##                      Edn.fctr^5                   Hhold.fctrPKn 
##                    0.2257899080                   -0.0791915425 
##                  Q100562.fctrNo                  Q100680.fctrNo 
##                    0.1226209054                    0.0283324887 
##                  Q101596.fctrNo                  Q106272.fctrNo 
##                    0.0339588645                    0.0042975876 
##                  Q112478.fctrNo                 Q114152.fctrYes 
##                    0.1327317705                   -0.1665812702 
##                  Q114517.fctrNo                 Q114517.fctrYes 
##                    0.0046106768                   -0.0127125638 
##                  Q115390.fctrNo                  Q115610.fctrNo 
##                    0.0280552504                    0.1660103263 
##                Q116197.fctrA.M.                  Q116953.fctrNo 
##                    0.1066048589                    0.0967694780 
##         Q117186.fctrCool headed      Q117193.fctrStandard hours 
##                    0.0090020902                    0.0313391640 
##                  Q118232.fctrId                  Q119851.fctrNo 
##                   -0.2629854510                    0.0365066033 
##         Q120194.fctrStudy first           Q120194.fctrTry first 
##                   -0.1400377316                    0.1488889687 
##                  Q121699.fctrNo                 Q121699.fctrYes 
##                    0.0746429895                   -0.0876316937 
##                  Q124742.fctrNo                   Q98197.fctrNo 
##                   -0.0172268361                   -0.2980400605 
##                  Q98197.fctrYes                  Q98578.fctrYes 
##                    0.0006860013                   -0.0999115235 
##                  YOB.Age.fctr^7  Hhold.fctrMKn:.clusterid.fctr2 
##                    0.0438543597                    0.0212298846 
##    Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff 
##                    1.9334073871                    0.0494671429 
## [1] "myfit_mdl: train diagnostics complete: 24.215000 secs"

##          Prediction
## Reference   D   R
##         D 558  32
##         R 103  39
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.155738e-01   2.720549e-01   7.855431e-01   8.430347e-01   8.060109e-01 
## AccuracyPValue  McnemarPValue 
##   2.737535e-01   1.694856e-09

##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   7.875648e-01   0.000000e+00   7.230441e-01   8.430301e-01   7.875648e-01 
## AccuracyPValue  McnemarPValue 
##   5.417029e-01   4.185437e-10 
## [1] "myfit_mdl: predict complete: 33.407000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     22.489                 9.096
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5105634            1   0.02112676        0.733027
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.25       0.3661972        0.8069228
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7855431             0.8430347    0.01021363
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.4967105    0.9934211            0       0.5511874
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.6               0        0.7875648
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.7230441             0.8430301             0
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.003715012      0.02424578
## [1] "myfit_mdl: exit: 33.608000 secs"
##                  label step_major step_minor label_minor     bgn     end
## 3   fit.models_1_All.X          1          2      glmnet 136.194 169.819
## 4 fit.models_1_preProc          1          3     preProc 169.820      NA
##   elapsed
## 3  33.625
## 4      NA
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet                      0.7875648       0.5511874
## Max.cor.Y.rcv.1X1###glmnet             0.7875648       0.5478979
## Low.cor.X##rcv#glmnet                  0.7875648       0.5465340
## Random###myrandom_classfr              0.7875648       0.5012035
## MFO###myMFO_classfr                    0.7875648       0.5000000
## Max.cor.Y##rcv#rpart                   0.7875648       0.5000000
## Interact.High.cor.Y##rcv#glmnet        0.7875648       0.4963094
##                                 max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet                     0.4967105                     22.489
## Max.cor.Y.rcv.1X1###glmnet            0.5000000                      0.788
## Low.cor.X##rcv#glmnet                 0.4967105                     26.852
## Random###myrandom_classfr             0.4922978                      0.267
## MFO###myMFO_classfr                   0.5000000                      0.429
## Max.cor.Y##rcv#rpart                  0.5000000                      1.576
## Interact.High.cor.Y##rcv#glmnet       0.5000000                      1.724
##                                 max.Accuracy.fit
## All.X##rcv#glmnet                      0.8069228
## Max.cor.Y.rcv.1X1###glmnet             0.8060109
## Low.cor.X##rcv#glmnet                  0.8069191
## Random###myrandom_classfr              0.8060109
## MFO###myMFO_classfr                    0.8060109
## Max.cor.Y##rcv#rpart                   0.8060121
## Interact.High.cor.Y##rcv#glmnet        0.8060121
##                  label step_major step_minor label_minor     bgn     end
## 4 fit.models_1_preProc          1          3     preProc 169.820 171.461
## 5     fit.models_1_end          1          4    teardown 171.462      NA
##   elapsed
## 4   1.641
## 5      NA
##        label step_major step_minor label_minor     bgn    end elapsed
## 5 fit.models          4          1           1 131.607 171.47  39.863
## 6 fit.models          4          2           2 171.471     NA      NA
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 172.591  NA      NA
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
## Warning: Removed 4 rows containing missing values (geom_errorbar).
## quartz_off_screen 
##                 2
## Warning: Removed 4 rows containing missing values (geom_errorbar).

##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 7               All.X##rcv#glmnet        0.7875648       0.5511874
## 3      Max.cor.Y.rcv.1X1###glmnet        0.7875648       0.5478979
## 6           Low.cor.X##rcv#glmnet        0.7875648       0.5465340
## 2       Random###myrandom_classfr        0.7875648       0.5012035
## 1             MFO###myMFO_classfr        0.7875648       0.5000000
## 4            Max.cor.Y##rcv#rpart        0.7875648       0.5000000
## 5 Interact.High.cor.Y##rcv#glmnet        0.7875648       0.4963094
##   max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 7       0.4967105                     22.489        0.8069228
## 3       0.5000000                      0.788        0.8060109
## 6       0.4967105                     26.852        0.8069191
## 2       0.4922978                      0.267        0.8060109
## 1       0.5000000                      0.429        0.8060109
## 4       0.5000000                      1.576        0.8060121
## 5       0.5000000                      1.724        0.8060121
##   opt.prob.threshold.fit opt.prob.threshold.OOB
## 7                   0.25                   0.60
## 3                   0.50                   0.50
## 6                   0.30                   0.65
## 2                   0.85                   0.85
## 1                   0.50                   0.50
## 4                   0.50                   0.50
## 5                   0.50                   0.50
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything - 
##     max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7ff32313ce98>
## [1] "Best model id: All.X##rcv#glmnet"
## glmnet 
## 
## 732 samples
## 108 predictors
##   2 classes: 'D', 'R' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times) 
## Summary of sample sizes: 488, 488, 488, 489, 488, 487, ... 
## Resampling results across tuning parameters:
## 
##   alpha  lambda       Accuracy   Kappa      
##   0.325  0.001034113  0.6780386  0.005105574
##   0.325  0.004799925  0.7163012  0.028942387
##   0.325  0.022279280  0.7750316  0.009287560
##   0.325  0.040000000  0.8014509  0.010640123
##   0.325  0.060000000  0.8064656  0.009310908
##   0.550  0.001034113  0.6780386  0.004857866
##   0.550  0.004799925  0.7249608  0.029862380
##   0.550  0.022279280  0.7946202  0.013337849
##   0.550  0.040000000  0.8069228  0.010213626
##   0.550  0.060000000  0.8060121  0.000000000
##   0.775  0.001034113  0.6794047  0.004871084
##   0.775  0.004799925  0.7367987  0.038752931
##   0.775  0.022279280  0.8028188  0.004958124
##   0.775  0.040000000  0.8069228  0.007440574
##   0.775  0.060000000  0.8060121  0.000000000
##   0.900  0.001034113  0.6771334  0.005132744
##   0.900  0.004799925  0.7358917  0.034105218
##   0.900  0.022279280  0.8055511  0.010332112
##   0.900  0.040000000  0.8060121  0.000000000
##   0.900  0.060000000  0.8060121  0.000000000
##   1.000  0.001034113  0.6798694  0.002621383
##   1.000  0.004799925  0.7427204  0.043018416
##   1.000  0.022279280  0.8069191  0.012986933
##   1.000  0.040000000  0.8060121  0.000000000
##   1.000  0.060000000  0.8060121  0.000000000
## 
## Accuracy was used to select the optimal model using  the largest value.
## The final values used for the model were alpha = 0.55 and lambda = 0.04.
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
##                                 All.X..rcv.glmnet.imp          imp
## Hhold.fctrN:.clusterid.fctr6             1.000000e+02 1.000000e+02
## Edn.fctr.Q                               1.745373e+01 1.745373e+01
## Q98197.fctrNo                            1.440277e+01 1.440277e+01
## Q118232.fctrId                           1.285848e+01 1.285848e+01
## Edn.fctr^5                               1.201560e+01 1.201560e+01
## Q114152.fctrYes                          7.764113e+00 7.764113e+00
## Q120194.fctrTry first                    7.718628e+00 7.718628e+00
## Q115610.fctrNo                           7.604491e+00 7.604491e+00
## Q120194.fctrStudy first                  6.689618e+00 6.689618e+00
## Q112478.fctrNo                           6.116418e+00 6.116418e+00
## Q100562.fctrNo                           5.132660e+00 5.132660e+00
## Q121699.fctrNo                           4.435750e+00 4.435750e+00
## Q116197.fctrA.M.                         4.410901e+00 4.410901e+00
## Q116953.fctrNo                           4.285022e+00 4.285022e+00
## Q98578.fctrYes                           3.896913e+00 3.896913e+00
## Q121699.fctrYes                          3.658572e+00 3.658572e+00
## Hhold.fctrPKn                            3.310930e+00 3.310930e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff          2.357253e+00 2.357253e+00
## Q117193.fctrStandard hours               7.941692e-01 7.941692e-01
## Q119851.fctrNo                           7.891620e-01 7.891620e-01
## YOB.Age.fctr^7                           5.141455e-01 5.141455e-01
## Q101596.fctrNo                           4.912269e-01 4.912269e-01
## Q100680.fctrNo                           4.005389e-01 4.005389e-01
## Q115390.fctrNo                           2.988845e-01 2.988845e-01
## Hhold.fctrMKn:.clusterid.fctr2           2.261710e-01 2.261710e-01
## Q124742.fctrNo                           1.835248e-01 1.835248e-01
## Q114517.fctrYes                          1.354323e-01 1.354323e-01
## Q117186.fctrCool headed                  9.590309e-02 9.590309e-02
## Q114517.fctrNo                           4.911950e-02 4.911950e-02
## Q106272.fctrNo                           4.578403e-02 4.578403e-02
## Q98197.fctrYes                           7.308264e-03 7.308264e-03
## .rnorm                                   0.000000e+00 0.000000e+00
## Edn.fctr.L                               0.000000e+00 0.000000e+00
## Edn.fctr.C                               0.000000e+00 0.000000e+00
## Edn.fctr^4                               0.000000e+00 0.000000e+00
## Edn.fctr^6                               0.000000e+00 0.000000e+00
## Edn.fctr^7                               0.000000e+00 0.000000e+00
## Gender.fctrF                             0.000000e+00 0.000000e+00
## Gender.fctrM                             0.000000e+00 0.000000e+00
## Hhold.fctrMKn                            0.000000e+00 0.000000e+00
## Hhold.fctrMKy                            0.000000e+00 0.000000e+00
## Hhold.fctrPKy                            0.000000e+00 0.000000e+00
## Hhold.fctrSKn                            0.000000e+00 0.000000e+00
## Hhold.fctrSKy                            0.000000e+00 0.000000e+00
## Income.fctr.L                            0.000000e+00 0.000000e+00
## Income.fctr.Q                            0.000000e+00 0.000000e+00
## Income.fctr.C                            0.000000e+00 0.000000e+00
## Income.fctr^4                            0.000000e+00 0.000000e+00
## Income.fctr^5                            0.000000e+00 0.000000e+00
## Income.fctr^6                            0.000000e+00 0.000000e+00
## Q100010.fctrNo                           0.000000e+00 0.000000e+00
## Q100010.fctrYes                          0.000000e+00 0.000000e+00
## Q100562.fctrYes                          0.000000e+00 0.000000e+00
## Q100680.fctrYes                          0.000000e+00 0.000000e+00
## Q100689.fctrNo                           0.000000e+00 0.000000e+00
## Q100689.fctrYes                          0.000000e+00 0.000000e+00
## Q101162.fctrOptimist                     0.000000e+00 0.000000e+00
## Q101162.fctrPessimist                    0.000000e+00 0.000000e+00
## Q101163.fctrDad                          0.000000e+00 0.000000e+00
## Q101163.fctrMom                          0.000000e+00 0.000000e+00
## Q101596.fctrYes                          0.000000e+00 0.000000e+00
## Q102089.fctrOwn                          0.000000e+00 0.000000e+00
## Q102089.fctrRent                         0.000000e+00 0.000000e+00
## Q102289.fctrNo                           0.000000e+00 0.000000e+00
## Q102289.fctrYes                          0.000000e+00 0.000000e+00
## Q102674.fctrNo                           0.000000e+00 0.000000e+00
## Q102674.fctrYes                          0.000000e+00 0.000000e+00
## Q102687.fctrNo                           0.000000e+00 0.000000e+00
## Q102687.fctrYes                          0.000000e+00 0.000000e+00
## Q102906.fctrNo                           0.000000e+00 0.000000e+00
## Q102906.fctrYes                          0.000000e+00 0.000000e+00
## Q103293.fctrNo                           0.000000e+00 0.000000e+00
## Q103293.fctrYes                          0.000000e+00 0.000000e+00
## Q104996.fctrNo                           0.000000e+00 0.000000e+00
## Q104996.fctrYes                          0.000000e+00 0.000000e+00
## Q105655.fctrNo                           0.000000e+00 0.000000e+00
## Q105655.fctrYes                          0.000000e+00 0.000000e+00
## Q105840.fctrNo                           0.000000e+00 0.000000e+00
## Q105840.fctrYes                          0.000000e+00 0.000000e+00
## Q106042.fctrNo                           0.000000e+00 0.000000e+00
## Q106042.fctrYes                          0.000000e+00 0.000000e+00
## Q106272.fctrYes                          0.000000e+00 0.000000e+00
## Q106388.fctrNo                           0.000000e+00 0.000000e+00
## Q106388.fctrYes                          0.000000e+00 0.000000e+00
## Q106389.fctrNo                           0.000000e+00 0.000000e+00
## Q106389.fctrYes                          0.000000e+00 0.000000e+00
## Q106993.fctrNo                           0.000000e+00 0.000000e+00
## Q106993.fctrYes                          0.000000e+00 0.000000e+00
## Q106997.fctrGr                           0.000000e+00 0.000000e+00
## Q106997.fctrYy                           0.000000e+00 0.000000e+00
## Q107491.fctrNo                           0.000000e+00 0.000000e+00
## Q107491.fctrYes                          0.000000e+00 0.000000e+00
## Q107869.fctrNo                           0.000000e+00 0.000000e+00
## Q107869.fctrYes                          0.000000e+00 0.000000e+00
## Q108342.fctrIn-person                    0.000000e+00 0.000000e+00
## Q108342.fctrOnline                       0.000000e+00 0.000000e+00
## Q108343.fctrNo                           0.000000e+00 0.000000e+00
## Q108343.fctrYes                          0.000000e+00 0.000000e+00
## Q108617.fctrNo                           0.000000e+00 0.000000e+00
## Q108617.fctrYes                          0.000000e+00 0.000000e+00
## Q108754.fctrNo                           0.000000e+00 0.000000e+00
## Q108754.fctrYes                          0.000000e+00 0.000000e+00
## Q108855.fctrUmm...                       0.000000e+00 0.000000e+00
## Q108855.fctrYes!                         0.000000e+00 0.000000e+00
## Q108856.fctrSocialize                    0.000000e+00 0.000000e+00
## Q108856.fctrSpace                        0.000000e+00 0.000000e+00
## Q108950.fctrCautious                     0.000000e+00 0.000000e+00
## Q108950.fctrRisk-friendly                0.000000e+00 0.000000e+00
## Q109367.fctrNo                           0.000000e+00 0.000000e+00
## Q109367.fctrYes                          0.000000e+00 0.000000e+00
## Q110740.fctrMac                          0.000000e+00 0.000000e+00
## Q110740.fctrPC                           0.000000e+00 0.000000e+00
## Q111220.fctrNo                           0.000000e+00 0.000000e+00
## Q111220.fctrYes                          0.000000e+00 0.000000e+00
## Q111580.fctrDemanding                    0.000000e+00 0.000000e+00
## Q111580.fctrSupportive                   0.000000e+00 0.000000e+00
## Q111848.fctrNo                           0.000000e+00 0.000000e+00
## Q111848.fctrYes                          0.000000e+00 0.000000e+00
## Q112270.fctrNo                           0.000000e+00 0.000000e+00
## Q112270.fctrYes                          0.000000e+00 0.000000e+00
## Q112478.fctrYes                          0.000000e+00 0.000000e+00
## Q112512.fctrNo                           0.000000e+00 0.000000e+00
## Q112512.fctrYes                          0.000000e+00 0.000000e+00
## Q113181.fctrNo                           0.000000e+00 0.000000e+00
## Q113181.fctrYes                          0.000000e+00 0.000000e+00
## Q113583.fctrTalk                         0.000000e+00 0.000000e+00
## Q113583.fctrTunes                        0.000000e+00 0.000000e+00
## Q113584.fctrPeople                       0.000000e+00 0.000000e+00
## Q113584.fctrTechnology                   0.000000e+00 0.000000e+00
## Q113992.fctrNo                           0.000000e+00 0.000000e+00
## Q113992.fctrYes                          0.000000e+00 0.000000e+00
## Q114152.fctrNo                           0.000000e+00 0.000000e+00
## Q114386.fctrMysterious                   0.000000e+00 0.000000e+00
## Q114386.fctrTMI                          0.000000e+00 0.000000e+00
## Q114748.fctrNo                           0.000000e+00 0.000000e+00
## Q114748.fctrYes                          0.000000e+00 0.000000e+00
## Q114961.fctrNo                           0.000000e+00 0.000000e+00
## Q114961.fctrYes                          0.000000e+00 0.000000e+00
## Q115195.fctrNo                           0.000000e+00 0.000000e+00
## Q115195.fctrYes                          0.000000e+00 0.000000e+00
## Q115390.fctrYes                          0.000000e+00 0.000000e+00
## Q115602.fctrNo                           0.000000e+00 0.000000e+00
## Q115602.fctrYes                          0.000000e+00 0.000000e+00
## Q115610.fctrYes                          0.000000e+00 0.000000e+00
## Q115611.fctrNo                           0.000000e+00 0.000000e+00
## Q115611.fctrYes                          0.000000e+00 0.000000e+00
## Q115777.fctrEnd                          0.000000e+00 0.000000e+00
## Q115777.fctrStart                        0.000000e+00 0.000000e+00
## Q115899.fctrCs                           0.000000e+00 0.000000e+00
## Q115899.fctrMe                           0.000000e+00 0.000000e+00
## Q116197.fctrP.M.                         0.000000e+00 0.000000e+00
## Q116441.fctrNo                           0.000000e+00 0.000000e+00
## Q116441.fctrYes                          0.000000e+00 0.000000e+00
## Q116448.fctrNo                           0.000000e+00 0.000000e+00
## Q116448.fctrYes                          0.000000e+00 0.000000e+00
## Q116601.fctrNo                           0.000000e+00 0.000000e+00
## Q116601.fctrYes                          0.000000e+00 0.000000e+00
## Q116797.fctrNo                           0.000000e+00 0.000000e+00
## Q116797.fctrYes                          0.000000e+00 0.000000e+00
## Q116881.fctrHappy                        0.000000e+00 0.000000e+00
## Q116881.fctrRight                        0.000000e+00 0.000000e+00
## Q116953.fctrYes                          0.000000e+00 0.000000e+00
## Q117186.fctrHot headed                   0.000000e+00 0.000000e+00
## Q117193.fctrOdd hours                    0.000000e+00 0.000000e+00
## Q118117.fctrNo                           0.000000e+00 0.000000e+00
## Q118117.fctrYes                          0.000000e+00 0.000000e+00
## Q118232.fctrPr                           0.000000e+00 0.000000e+00
## Q118233.fctrNo                           0.000000e+00 0.000000e+00
## Q118233.fctrYes                          0.000000e+00 0.000000e+00
## Q118237.fctrNo                           0.000000e+00 0.000000e+00
## Q118237.fctrYes                          0.000000e+00 0.000000e+00
## Q118892.fctrNo                           0.000000e+00 0.000000e+00
## Q118892.fctrYes                          0.000000e+00 0.000000e+00
## Q119334.fctrNo                           0.000000e+00 0.000000e+00
## Q119334.fctrYes                          0.000000e+00 0.000000e+00
## Q119650.fctrGiving                       0.000000e+00 0.000000e+00
## Q119650.fctrReceiving                    0.000000e+00 0.000000e+00
## Q119851.fctrYes                          0.000000e+00 0.000000e+00
## Q120012.fctrNo                           0.000000e+00 0.000000e+00
## Q120012.fctrYes                          0.000000e+00 0.000000e+00
## Q120014.fctrNo                           0.000000e+00 0.000000e+00
## Q120014.fctrYes                          0.000000e+00 0.000000e+00
## Q120379.fctrNo                           0.000000e+00 0.000000e+00
## Q120379.fctrYes                          0.000000e+00 0.000000e+00
## Q120472.fctrArt                          0.000000e+00 0.000000e+00
## Q120472.fctrScience                      0.000000e+00 0.000000e+00
## Q120650.fctrNo                           0.000000e+00 0.000000e+00
## Q120650.fctrYes                          0.000000e+00 0.000000e+00
## Q120978.fctrNo                           0.000000e+00 0.000000e+00
## Q120978.fctrYes                          0.000000e+00 0.000000e+00
## Q121011.fctrNo                           0.000000e+00 0.000000e+00
## Q121011.fctrYes                          0.000000e+00 0.000000e+00
## Q121700.fctrNo                           0.000000e+00 0.000000e+00
## Q121700.fctrYes                          0.000000e+00 0.000000e+00
## Q122120.fctrNo                           0.000000e+00 0.000000e+00
## Q122120.fctrYes                          0.000000e+00 0.000000e+00
## Q122769.fctrNo                           0.000000e+00 0.000000e+00
## Q122769.fctrYes                          0.000000e+00 0.000000e+00
## Q122770.fctrNo                           0.000000e+00 0.000000e+00
## Q122770.fctrYes                          0.000000e+00 0.000000e+00
## Q122771.fctrPc                           0.000000e+00 0.000000e+00
## Q122771.fctrPt                           0.000000e+00 0.000000e+00
## Q123464.fctrNo                           0.000000e+00 0.000000e+00
## Q123464.fctrYes                          0.000000e+00 0.000000e+00
## Q123621.fctrNo                           0.000000e+00 0.000000e+00
## Q123621.fctrYes                          0.000000e+00 0.000000e+00
## Q124122.fctrNo                           0.000000e+00 0.000000e+00
## Q124122.fctrYes                          0.000000e+00 0.000000e+00
## Q124742.fctrYes                          0.000000e+00 0.000000e+00
## Q96024.fctrNo                            0.000000e+00 0.000000e+00
## Q96024.fctrYes                           0.000000e+00 0.000000e+00
## Q98059.fctrOnly-child                    0.000000e+00 0.000000e+00
## Q98059.fctrYes                           0.000000e+00 0.000000e+00
## Q98078.fctrNo                            0.000000e+00 0.000000e+00
## Q98078.fctrYes                           0.000000e+00 0.000000e+00
## Q98578.fctrNo                            0.000000e+00 0.000000e+00
## Q98869.fctrNo                            0.000000e+00 0.000000e+00
## Q98869.fctrYes                           0.000000e+00 0.000000e+00
## Q99480.fctrNo                            0.000000e+00 0.000000e+00
## Q99480.fctrYes                           0.000000e+00 0.000000e+00
## Q99581.fctrNo                            0.000000e+00 0.000000e+00
## Q99581.fctrYes                           0.000000e+00 0.000000e+00
## Q99716.fctrNo                            0.000000e+00 0.000000e+00
## Q99716.fctrYes                           0.000000e+00 0.000000e+00
## Q99982.fctrCheck!                        0.000000e+00 0.000000e+00
## Q99982.fctrNope                          0.000000e+00 0.000000e+00
## YOB.Age.fctr.L                           0.000000e+00 0.000000e+00
## YOB.Age.fctr.Q                           0.000000e+00 0.000000e+00
## YOB.Age.fctr.C                           0.000000e+00 0.000000e+00
## YOB.Age.fctr^4                           0.000000e+00 0.000000e+00
## YOB.Age.fctr^5                           0.000000e+00 0.000000e+00
## YOB.Age.fctr^6                           0.000000e+00 0.000000e+00
## YOB.Age.fctr^8                           0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr2             0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr2           0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr2           0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr2           0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr2           0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2           0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr3             0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr3           0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr4             0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4           0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr5             0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr5           0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr6           0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr6           0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr6           0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr6           0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr6           0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr6           0.000000e+00 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff               0.000000e+00 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff          0.000000e+00 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff          0.000000e+00 0.000000e+00
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    3895          R                         0.1177019
## 2    2446          R                         0.1267051
## 3    2749          R                         0.1558417
## 4    4762          R                         0.1639864
## 5    5800          R                         0.1704307
## 6    5782          R                         0.1969376
## 7    1345          D                         0.1348319
## 8    2882          D                         0.1166666
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
## 7                            D                            FALSE
## 8                            D                            FALSE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.8822981                               FALSE
## 2                            0.8732949                               FALSE
## 3                            0.8441583                               FALSE
## 4                            0.8360136                               FALSE
## 5                            0.8295693                               FALSE
## 6                            0.8030624                               FALSE
## 7                            0.1348319                                TRUE
## 8                            0.1166666                                TRUE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.4822981
## 2                                 FALSE                         -0.4732949
## 3                                 FALSE                         -0.4441583
## 4                                 FALSE                         -0.4360136
## 5                                 FALSE                         -0.4295693
## 6                                 FALSE                         -0.4030624
## 7                                  TRUE                          0.0000000
## 8                                  TRUE                          0.0000000
##   .label
## 1   3895
## 2   2446
## 3   2749
## 4   4762
## 5   5800
## 6   5782
## 7   1345
## 8   2882
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    3895          R                         0.1177019
## 2     626          R                         0.1235382
## 3    2446          R                         0.1267051
## 4    3212          R                         0.1332330
## 5    1883          R                         0.1341627
## 6    4264          R                         0.1512476
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.8822981                               FALSE
## 2                            0.8764618                               FALSE
## 3                            0.8732949                               FALSE
## 4                            0.8667670                               FALSE
## 5                            0.8658373                               FALSE
## 6                            0.8487524                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.4822981
## 2                                 FALSE                         -0.4764618
## 3                                 FALSE                         -0.4732949
## 4                                 FALSE                         -0.4667670
## 5                                 FALSE                         -0.4658373
## 6                                 FALSE                         -0.4487524
##    USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 10    1352          R                         0.1605493
## 26    3262          R                         0.2099293
## 28    6045          R                         0.2115404
## 32    2252          R                         0.2300111
## 37    6135          R                         0.2511529
## 41     445          R                         0.2719164
##    Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 10                            D                             TRUE
## 26                            D                             TRUE
## 28                            D                             TRUE
## 32                            D                             TRUE
## 37                            D                             TRUE
## 41                            D                             TRUE
##    Party.fctr.All.X..rcv.glmnet.err.abs
## 10                            0.8394507
## 26                            0.7900707
## 28                            0.7884596
## 32                            0.7699889
## 37                            0.7488471
## 41                            0.7280836
##    Party.fctr.All.X..rcv.glmnet.is.acc
## 10                               FALSE
## 26                               FALSE
## 28                               FALSE
## 32                               FALSE
## 37                               FALSE
## 41                               FALSE
##    Party.fctr.All.X..rcv.glmnet.accurate
## 10                                 FALSE
## 26                                 FALSE
## 28                                 FALSE
## 32                                 FALSE
## 37                                 FALSE
## 41                                 FALSE
##    Party.fctr.All.X..rcv.glmnet.error
## 10                         -0.4394507
## 26                         -0.3900707
## 28                         -0.3884596
## 32                         -0.3699889
## 37                         -0.3488471
## 41                         -0.3280836
##    USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 36    4010          R                         0.2478407
## 37    6135          R                         0.2511529
## 38    3640          R                         0.2565594
## 39    5466          R                         0.2611470
## 40    2799          R                         0.2659742
## 41     445          R                         0.2719164
##    Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 36                            D                             TRUE
## 37                            D                             TRUE
## 38                            D                             TRUE
## 39                            D                             TRUE
## 40                            D                             TRUE
## 41                            D                             TRUE
##    Party.fctr.All.X..rcv.glmnet.err.abs
## 36                            0.7521593
## 37                            0.7488471
## 38                            0.7434406
## 39                            0.7388530
## 40                            0.7340258
## 41                            0.7280836
##    Party.fctr.All.X..rcv.glmnet.is.acc
## 36                               FALSE
## 37                               FALSE
## 38                               FALSE
## 39                               FALSE
## 40                               FALSE
## 41                               FALSE
##    Party.fctr.All.X..rcv.glmnet.accurate
## 36                                 FALSE
## 37                                 FALSE
## 38                                 FALSE
## 39                                 FALSE
## 40                                 FALSE
## 41                                 FALSE
##    Party.fctr.All.X..rcv.glmnet.error
## 36                         -0.3521593
## 37                         -0.3488471
## 38                         -0.3434406
## 39                         -0.3388530
## 40                         -0.3340258
## 41                         -0.3280836

##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      3      8      2     0.01092896     0.01554404
## N            N      9     40     10     0.05464481     0.04663212
## MKy        MKy     47    182     55     0.24863388     0.24352332
## SKy        SKy      9     37     10     0.05054645     0.04663212
## MKn        MKn     23    100     26     0.13661202     0.11917098
## SKn        SKn     93    324    110     0.44262295     0.48186528
## PKn        PKn      9     41     10     0.05601093     0.04663212
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy     0.00896861        1.415925        0.1769906      8        1.243398
## N       0.04484305       10.618246        0.2654561     40        3.510021
## MKy     0.24663677       52.182641        0.2867178    182       15.160971
## SKy     0.04484305        9.444703        0.2552622     37        2.878612
## MKn     0.11659193       31.035907        0.3103591    100        7.342244
## SKn     0.49327354      105.501365        0.3256215    324       29.134241
## PKn     0.04484305        8.686566        0.2118675     41        2.779787
##     err.abs.OOB.mean
## PKy        0.4144660
## N          0.3900023
## MKy        0.3225738
## SKy        0.3198458
## MKn        0.3192280
## SKn        0.3132714
## PKn        0.3088652
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       193.000000       732.000000       223.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       218.885353         1.832275 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##       732.000000        62.049273         2.388253
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 178.921  NA      NA
##        label step_major step_minor label_minor     bgn    end elapsed
## 6 fit.models          4          2           2 171.471 178.93   7.459
## 7 fit.models          4          3           3 178.930     NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##               label step_major step_minor label_minor     bgn     end
## 7        fit.models          4          3           3 178.930 181.846
## 8 fit.data.training          5          0           0 181.847      NA
##   elapsed
## 7   2.916
## 8      NA

Step 5.0: fit data training

```{r fit.data.training_0, cache=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X###glmnet"
## [1] "    indepVar: Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.737000 secs"
## Fitting alpha = 0.55, lambda = 0.04 on full training set
## [1] "myfit_mdl: train complete: 3.250000 secs"
##   alpha lambda
## 1  0.55   0.04

##             Length Class      Mode     
## a0             86  -none-     numeric  
## beta        23564  dgCMatrix  S4       
## df             86  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         86  -none-     numeric  
## dev.ratio      86  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        274  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                    -1.247949359                    -0.135662592 
##                      Edn.fctr^5                  Q100680.fctrNo 
##                     0.137102512                     0.044543376 
##                  Q100689.fctrNo                 Q110740.fctrMac 
##                     0.018798140                    -0.037253725 
##                  Q112478.fctrNo                 Q114152.fctrYes 
##                     0.105380230                    -0.073889597 
##                 Q114517.fctrYes                 Q115611.fctrYes 
##                    -0.005237977                     0.001657426 
##                Q116197.fctrA.M.                  Q116953.fctrNo 
##                     0.047669095                     0.009812758 
##                  Q118232.fctrId                  Q119851.fctrNo 
##                    -0.251720827                     0.010679633 
##         Q120194.fctrStudy first           Q120194.fctrTry first 
##                    -0.142922459                     0.017473595 
##                  Q121699.fctrNo                 Q124122.fctrYes 
##                     0.077677438                    -0.012522799 
##                  Q124742.fctrNo                   Q98197.fctrNo 
##                    -0.040963097                    -0.108040223 
##                  YOB.Age.fctr^7  Hhold.fctrMKn:.clusterid.fctr2 
##                     0.044813073                     0.047062161 
##    Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff 
##                     0.861171495                     0.007886025 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                      Edn.fctr.Q 
##                    -1.240309458                    -0.181216995 
##                      Edn.fctr.C                      Edn.fctr^5 
##                     0.021575515                     0.158619647 
##                  Q100680.fctrNo                  Q100689.fctrNo 
##                     0.065498098                     0.032602770 
##                 Q110740.fctrMac                  Q112270.fctrNo 
##                    -0.057209954                    -0.003871040 
##                  Q112478.fctrNo                 Q114152.fctrYes 
##                     0.129215957                    -0.103656048 
##                 Q114517.fctrYes                 Q115611.fctrYes 
##                    -0.037406135                     0.030322176 
##                Q116197.fctrA.M.                Q116197.fctrP.M. 
##                     0.076472844                    -0.003296449 
##                  Q116953.fctrNo         Q117186.fctrCool headed 
##                     0.034222174                     0.023049342 
##                  Q118232.fctrId                  Q119851.fctrNo 
##                    -0.288995464                     0.040128102 
##         Q120194.fctrStudy first           Q120194.fctrTry first 
##                    -0.163057233                     0.033305627 
##                  Q121699.fctrNo                 Q124122.fctrYes 
##                     0.088978128                    -0.028607823 
##                  Q124742.fctrNo                   Q98197.fctrNo 
##                    -0.069527698                    -0.142051798 
##                  YOB.Age.fctr^7  Hhold.fctrMKn:.clusterid.fctr2 
##                     0.086250907                     0.113875995 
##    Hhold.fctrN:.clusterid.fctr6 YOB.Age.fctr(15,20]:YOB.Age.dff 
##                     0.971013197                     0.011553732 
## [1] "myfit_mdl: train diagnostics complete: 3.323000 secs"

##          Prediction
## Reference   D   R
##         D 722  20
##         R 158  25
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   8.075676e-01   1.531657e-01   7.806592e-01   8.325006e-01   8.021622e-01 
## AccuracyPValue  McnemarPValue 
##   3.578570e-01   9.762690e-25 
## [1] "myfit_mdl: predict complete: 8.136000 secs"
##                     id
## 1 Final.All.X###glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Gender.fctr,Q108754.fctr,Q108856.fctr,Q120014.fctr,Q115611.fctr,Q102906.fctr,Q101596.fctr,Q120194.fctr,Hhold.fctr,Q99480.fctr,Q108343.fctr,Q108617.fctr,Q108855.fctr,Q109367.fctr,Q117193.fctr,Q99982.fctr,Q114748.fctr,Q111580.fctr,Q98197.fctr,Q101163.fctr,Q102289.fctr,Q116881.fctr,Q101162.fctr,Q102674.fctr,Q102089.fctr,Q118232.fctr,Q118117.fctr,Q99581.fctr,Q108342.fctr,Q113584.fctr,Edn.fctr,Q113181.fctr,Q115899.fctr,Q122771.fctr,Q106388.fctr,Q113583.fctr,Q119334.fctr,Q105655.fctr,Q115777.fctr,Q98869.fctr,Q115602.fctr,Q107869.fctr,.rnorm,Q120472.fctr,Q100562.fctr,Q115610.fctr,Q121700.fctr,Q106042.fctr,Q116441.fctr,Q119650.fctr,Q120978.fctr,Income.fctr,Q99716.fctr,Q102687.fctr,Q107491.fctr,Q100010.fctr,Q112270.fctr,Q123464.fctr,Q104996.fctr,Q116797.fctr,Q116601.fctr,Q116953.fctr,Q110740.fctr,Q103293.fctr,Q122120.fctr,Q108950.fctr,Q100680.fctr,Q122769.fctr,Q106993.fctr,Q111848.fctr,Q121011.fctr,Q115195.fctr,Q120650.fctr,Q96024.fctr,Q112512.fctr,Q118233.fctr,Q116448.fctr,Q106389.fctr,Q118237.fctr,Q124742.fctr,Q111220.fctr,Q117186.fctr,Q106272.fctr,Q98059.fctr,Q120379.fctr,Q105840.fctr,Q114961.fctr,Q98578.fctr,Q122770.fctr,Q106997.fctr,YOB.Age.fctr,Q98078.fctr,Q100689.fctr,Q119851.fctr,Q112478.fctr,Q118892.fctr,Q116197.fctr,Q113992.fctr,Q123621.fctr,Q120012.fctr,Q115390.fctr,Q121699.fctr,Q114517.fctr,Q124122.fctr,Q114386.fctr,Q114152.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               0                      2.429                 1.583
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1             0.5            1            0       0.6988497
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.25       0.2192982        0.8075676
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.7806592             0.8325006     0.1531657
## [1] "myfit_mdl: exit: 8.158000 secs"
##               label step_major step_minor label_minor     bgn     end
## 8 fit.data.training          5          0           0 181.847 190.473
## 9 fit.data.training          5          1           1 190.474      NA
##   elapsed
## 8   8.627
## 9      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification)
    #     mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    # mdlEnsembleComps <- gsub(paste0("^", 
    #                     gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
    #                          "", mdlEnsembleComps)
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlEnsembleComps)] else
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$value  %in% mdlEnsembleComps)]
                        
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        # glb_fin_mdl uses the same coefficients as glb_sel_mdl, 
        #   so copy the "Final" columns into "non-Final" columns
        glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
        glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.6
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                                 All.X..rcv.glmnet.imp
## Hhold.fctrN:.clusterid.fctr6             1.000000e+02
## Q118232.fctrId                           1.285848e+01
## Edn.fctr.Q                               1.745373e+01
## Q120194.fctrStudy first                  6.689618e+00
## Edn.fctr^5                               1.201560e+01
## Q98197.fctrNo                            1.440277e+01
## Q112478.fctrNo                           6.116418e+00
## Q114152.fctrYes                          7.764113e+00
## Q121699.fctrNo                           4.435750e+00
## Hhold.fctrMKn:.clusterid.fctr2           2.261710e-01
## YOB.Age.fctr^7                           5.141455e-01
## Q116197.fctrA.M.                         4.410901e+00
## Q100680.fctrNo                           4.005389e-01
## Q124742.fctrNo                           1.835248e-01
## Q110740.fctrMac                          0.000000e+00
## Q100689.fctrNo                           0.000000e+00
## Q120194.fctrTry first                    7.718628e+00
## Q119851.fctrNo                           7.891620e-01
## Q124122.fctrYes                          0.000000e+00
## Q116953.fctrNo                           4.285022e+00
## Q114517.fctrYes                          1.354323e-01
## Q115611.fctrYes                          0.000000e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff          2.357253e+00
## Q117186.fctrCool headed                  9.590309e-02
## Edn.fctr.C                               0.000000e+00
## Q112270.fctrNo                           0.000000e+00
## Q116197.fctrP.M.                         0.000000e+00
## .rnorm                                   0.000000e+00
## Edn.fctr.L                               0.000000e+00
## Edn.fctr^4                               0.000000e+00
## Edn.fctr^6                               0.000000e+00
## Edn.fctr^7                               0.000000e+00
## Gender.fctrF                             0.000000e+00
## Gender.fctrM                             0.000000e+00
## Hhold.fctrMKn                            0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr3           0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr4           0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr5           0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr6           0.000000e+00
## Hhold.fctrMKy                            0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr2           0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr3           0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr4           0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr5           0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr6           0.000000e+00
## Hhold.fctrN:.clusterid.fctr2             0.000000e+00
## Hhold.fctrN:.clusterid.fctr3             0.000000e+00
## Hhold.fctrN:.clusterid.fctr4             0.000000e+00
## Hhold.fctrN:.clusterid.fctr5             0.000000e+00
## Hhold.fctrPKn                            3.310930e+00
## Hhold.fctrPKn:.clusterid.fctr2           0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3           0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4           0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr5           0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr6           0.000000e+00
## Hhold.fctrPKy                            0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr2           0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr3           0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4           0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr5           0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr6           0.000000e+00
## Hhold.fctrSKn                            0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr2           0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr3           0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4           0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr5           0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr6           0.000000e+00
## Hhold.fctrSKy                            0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2           0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr3           0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4           0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr5           0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr6           0.000000e+00
## Income.fctr.C                            0.000000e+00
## Income.fctr.L                            0.000000e+00
## Income.fctr.Q                            0.000000e+00
## Income.fctr^4                            0.000000e+00
## Income.fctr^5                            0.000000e+00
## Income.fctr^6                            0.000000e+00
## Q100010.fctrNo                           0.000000e+00
## Q100010.fctrYes                          0.000000e+00
## Q100562.fctrNo                           5.132660e+00
## Q100562.fctrYes                          0.000000e+00
## Q100680.fctrYes                          0.000000e+00
## Q100689.fctrYes                          0.000000e+00
## Q101162.fctrOptimist                     0.000000e+00
## Q101162.fctrPessimist                    0.000000e+00
## Q101163.fctrDad                          0.000000e+00
## Q101163.fctrMom                          0.000000e+00
## Q101596.fctrNo                           4.912269e-01
## Q101596.fctrYes                          0.000000e+00
## Q102089.fctrOwn                          0.000000e+00
## Q102089.fctrRent                         0.000000e+00
## Q102289.fctrNo                           0.000000e+00
## Q102289.fctrYes                          0.000000e+00
## Q102674.fctrNo                           0.000000e+00
## Q102674.fctrYes                          0.000000e+00
## Q102687.fctrNo                           0.000000e+00
## Q102687.fctrYes                          0.000000e+00
## Q102906.fctrNo                           0.000000e+00
## Q102906.fctrYes                          0.000000e+00
## Q103293.fctrNo                           0.000000e+00
## Q103293.fctrYes                          0.000000e+00
## Q104996.fctrNo                           0.000000e+00
## Q104996.fctrYes                          0.000000e+00
## Q105655.fctrNo                           0.000000e+00
## Q105655.fctrYes                          0.000000e+00
## Q105840.fctrNo                           0.000000e+00
## Q105840.fctrYes                          0.000000e+00
## Q106042.fctrNo                           0.000000e+00
## Q106042.fctrYes                          0.000000e+00
## Q106272.fctrNo                           4.578403e-02
## Q106272.fctrYes                          0.000000e+00
## Q106388.fctrNo                           0.000000e+00
## Q106388.fctrYes                          0.000000e+00
## Q106389.fctrNo                           0.000000e+00
## Q106389.fctrYes                          0.000000e+00
## Q106993.fctrNo                           0.000000e+00
## Q106993.fctrYes                          0.000000e+00
## Q106997.fctrGr                           0.000000e+00
## Q106997.fctrYy                           0.000000e+00
## Q107491.fctrNo                           0.000000e+00
## Q107491.fctrYes                          0.000000e+00
## Q107869.fctrNo                           0.000000e+00
## Q107869.fctrYes                          0.000000e+00
## Q108342.fctrIn-person                    0.000000e+00
## Q108342.fctrOnline                       0.000000e+00
## Q108343.fctrNo                           0.000000e+00
## Q108343.fctrYes                          0.000000e+00
## Q108617.fctrNo                           0.000000e+00
## Q108617.fctrYes                          0.000000e+00
## Q108754.fctrNo                           0.000000e+00
## Q108754.fctrYes                          0.000000e+00
## Q108855.fctrUmm...                       0.000000e+00
## Q108855.fctrYes!                         0.000000e+00
## Q108856.fctrSocialize                    0.000000e+00
## Q108856.fctrSpace                        0.000000e+00
## Q108950.fctrCautious                     0.000000e+00
## Q108950.fctrRisk-friendly                0.000000e+00
## Q109367.fctrNo                           0.000000e+00
## Q109367.fctrYes                          0.000000e+00
## Q110740.fctrPC                           0.000000e+00
## Q111220.fctrNo                           0.000000e+00
## Q111220.fctrYes                          0.000000e+00
## Q111580.fctrDemanding                    0.000000e+00
## Q111580.fctrSupportive                   0.000000e+00
## Q111848.fctrNo                           0.000000e+00
## Q111848.fctrYes                          0.000000e+00
## Q112270.fctrYes                          0.000000e+00
## Q112478.fctrYes                          0.000000e+00
## Q112512.fctrNo                           0.000000e+00
## Q112512.fctrYes                          0.000000e+00
## Q113181.fctrNo                           0.000000e+00
## Q113181.fctrYes                          0.000000e+00
## Q113583.fctrTalk                         0.000000e+00
## Q113583.fctrTunes                        0.000000e+00
## Q113584.fctrPeople                       0.000000e+00
## Q113584.fctrTechnology                   0.000000e+00
## Q113992.fctrNo                           0.000000e+00
## Q113992.fctrYes                          0.000000e+00
## Q114152.fctrNo                           0.000000e+00
## Q114386.fctrMysterious                   0.000000e+00
## Q114386.fctrTMI                          0.000000e+00
## Q114517.fctrNo                           4.911950e-02
## Q114748.fctrNo                           0.000000e+00
## Q114748.fctrYes                          0.000000e+00
## Q114961.fctrNo                           0.000000e+00
## Q114961.fctrYes                          0.000000e+00
## Q115195.fctrNo                           0.000000e+00
## Q115195.fctrYes                          0.000000e+00
## Q115390.fctrNo                           2.988845e-01
## Q115390.fctrYes                          0.000000e+00
## Q115602.fctrNo                           0.000000e+00
## Q115602.fctrYes                          0.000000e+00
## Q115610.fctrNo                           7.604491e+00
## Q115610.fctrYes                          0.000000e+00
## Q115611.fctrNo                           0.000000e+00
## Q115777.fctrEnd                          0.000000e+00
## Q115777.fctrStart                        0.000000e+00
## Q115899.fctrCs                           0.000000e+00
## Q115899.fctrMe                           0.000000e+00
## Q116441.fctrNo                           0.000000e+00
## Q116441.fctrYes                          0.000000e+00
## Q116448.fctrNo                           0.000000e+00
## Q116448.fctrYes                          0.000000e+00
## Q116601.fctrNo                           0.000000e+00
## Q116601.fctrYes                          0.000000e+00
## Q116797.fctrNo                           0.000000e+00
## Q116797.fctrYes                          0.000000e+00
## Q116881.fctrHappy                        0.000000e+00
## Q116881.fctrRight                        0.000000e+00
## Q116953.fctrYes                          0.000000e+00
## Q117186.fctrHot headed                   0.000000e+00
## Q117193.fctrOdd hours                    0.000000e+00
## Q117193.fctrStandard hours               7.941692e-01
## Q118117.fctrNo                           0.000000e+00
## Q118117.fctrYes                          0.000000e+00
## Q118232.fctrPr                           0.000000e+00
## Q118233.fctrNo                           0.000000e+00
## Q118233.fctrYes                          0.000000e+00
## Q118237.fctrNo                           0.000000e+00
## Q118237.fctrYes                          0.000000e+00
## Q118892.fctrNo                           0.000000e+00
## Q118892.fctrYes                          0.000000e+00
## Q119334.fctrNo                           0.000000e+00
## Q119334.fctrYes                          0.000000e+00
## Q119650.fctrGiving                       0.000000e+00
## Q119650.fctrReceiving                    0.000000e+00
## Q119851.fctrYes                          0.000000e+00
## Q120012.fctrNo                           0.000000e+00
## Q120012.fctrYes                          0.000000e+00
## Q120014.fctrNo                           0.000000e+00
## Q120014.fctrYes                          0.000000e+00
## Q120379.fctrNo                           0.000000e+00
## Q120379.fctrYes                          0.000000e+00
## Q120472.fctrArt                          0.000000e+00
## Q120472.fctrScience                      0.000000e+00
## Q120650.fctrNo                           0.000000e+00
## Q120650.fctrYes                          0.000000e+00
## Q120978.fctrNo                           0.000000e+00
## Q120978.fctrYes                          0.000000e+00
## Q121011.fctrNo                           0.000000e+00
## Q121011.fctrYes                          0.000000e+00
## Q121699.fctrYes                          3.658572e+00
## Q121700.fctrNo                           0.000000e+00
## Q121700.fctrYes                          0.000000e+00
## Q122120.fctrNo                           0.000000e+00
## Q122120.fctrYes                          0.000000e+00
## Q122769.fctrNo                           0.000000e+00
## Q122769.fctrYes                          0.000000e+00
## Q122770.fctrNo                           0.000000e+00
## Q122770.fctrYes                          0.000000e+00
## Q122771.fctrPc                           0.000000e+00
## Q122771.fctrPt                           0.000000e+00
## Q123464.fctrNo                           0.000000e+00
## Q123464.fctrYes                          0.000000e+00
## Q123621.fctrNo                           0.000000e+00
## Q123621.fctrYes                          0.000000e+00
## Q124122.fctrNo                           0.000000e+00
## Q124742.fctrYes                          0.000000e+00
## Q96024.fctrNo                            0.000000e+00
## Q96024.fctrYes                           0.000000e+00
## Q98059.fctrOnly-child                    0.000000e+00
## Q98059.fctrYes                           0.000000e+00
## Q98078.fctrNo                            0.000000e+00
## Q98078.fctrYes                           0.000000e+00
## Q98197.fctrYes                           7.308264e-03
## Q98578.fctrNo                            0.000000e+00
## Q98578.fctrYes                           3.896913e+00
## Q98869.fctrNo                            0.000000e+00
## Q98869.fctrYes                           0.000000e+00
## Q99480.fctrNo                            0.000000e+00
## Q99480.fctrYes                           0.000000e+00
## Q99581.fctrNo                            0.000000e+00
## Q99581.fctrYes                           0.000000e+00
## Q99716.fctrNo                            0.000000e+00
## Q99716.fctrYes                           0.000000e+00
## Q99982.fctrCheck!                        0.000000e+00
## Q99982.fctrNope                          0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff          0.000000e+00
## YOB.Age.fctr.C                           0.000000e+00
## YOB.Age.fctr.L                           0.000000e+00
## YOB.Age.fctr.Q                           0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff               0.000000e+00
## YOB.Age.fctr^4                           0.000000e+00
## YOB.Age.fctr^5                           0.000000e+00
## YOB.Age.fctr^6                           0.000000e+00
## YOB.Age.fctr^8                           0.000000e+00
##                                 Final.All.X...glmnet.imp         imp
## Hhold.fctrN:.clusterid.fctr6                 100.0000000 100.0000000
## Q118232.fctrId                                29.4152402  29.4152402
## Edn.fctr.Q                                    16.7656471  16.7656471
## Q120194.fctrStudy first                       16.6645549  16.6645549
## Edn.fctr^5                                    16.0648774  16.0648774
## Q98197.fctrNo                                 13.2707242  13.2707242
## Q112478.fctrNo                                12.6093429  12.6093429
## Q114152.fctrYes                                9.3090924   9.3090924
## Q121699.fctrNo                                 9.0698923   9.0698923
## Hhold.fctrMKn:.clusterid.fctr2                 7.6441098   7.6441098
## YOB.Age.fctr^7                                 6.4838565   6.4838565
## Q116197.fctrA.M.                               6.3496943   6.3496943
## Q100680.fctrNo                                 5.7197445   5.7197445
## Q124742.fctrNo                                 5.5930697   5.5930697
## Q110740.fctrMac                                4.8708017   4.8708017
## Q100689.fctrNo                                 2.5916328   2.5916328
## Q120194.fctrTry first                          2.5165332   2.5165332
## Q119851.fctrNo                                 2.2466212   2.2466212
## Q124122.fctrYes                                1.9733377   1.9733377
## Q116953.fctrNo                                 1.9693431   1.9693431
## Q114517.fctrYes                                1.7370660   1.7370660
## Q115611.fctrYes                                1.2121081   1.2121081
## YOB.Age.fctr(15,20]:YOB.Age.dff                1.0111216   1.0111216
## Q117186.fctrCool headed                        0.8259899   0.8259899
## Edn.fctr.C                                     0.7731743   0.7731743
## Q112270.fctrNo                                 0.1387215   0.1387215
## Q116197.fctrP.M.                               0.1181306   0.1181306
## .rnorm                                         0.0000000   0.0000000
## Edn.fctr.L                                     0.0000000   0.0000000
## Edn.fctr^4                                     0.0000000   0.0000000
## Edn.fctr^6                                     0.0000000   0.0000000
## Edn.fctr^7                                     0.0000000   0.0000000
## Gender.fctrF                                   0.0000000   0.0000000
## Gender.fctrM                                   0.0000000   0.0000000
## Hhold.fctrMKn                                  0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrMKn:.clusterid.fctr6                 0.0000000   0.0000000
## Hhold.fctrMKy                                  0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrMKy:.clusterid.fctr6                 0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr2                   0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr3                   0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr4                   0.0000000   0.0000000
## Hhold.fctrN:.clusterid.fctr5                   0.0000000   0.0000000
## Hhold.fctrPKn                                  0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrPKn:.clusterid.fctr6                 0.0000000   0.0000000
## Hhold.fctrPKy                                  0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrPKy:.clusterid.fctr6                 0.0000000   0.0000000
## Hhold.fctrSKn                                  0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrSKn:.clusterid.fctr6                 0.0000000   0.0000000
## Hhold.fctrSKy                                  0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr2                 0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr3                 0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr4                 0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr5                 0.0000000   0.0000000
## Hhold.fctrSKy:.clusterid.fctr6                 0.0000000   0.0000000
## Income.fctr.C                                  0.0000000   0.0000000
## Income.fctr.L                                  0.0000000   0.0000000
## Income.fctr.Q                                  0.0000000   0.0000000
## Income.fctr^4                                  0.0000000   0.0000000
## Income.fctr^5                                  0.0000000   0.0000000
## Income.fctr^6                                  0.0000000   0.0000000
## Q100010.fctrNo                                 0.0000000   0.0000000
## Q100010.fctrYes                                0.0000000   0.0000000
## Q100562.fctrNo                                 0.0000000   0.0000000
## Q100562.fctrYes                                0.0000000   0.0000000
## Q100680.fctrYes                                0.0000000   0.0000000
## Q100689.fctrYes                                0.0000000   0.0000000
## Q101162.fctrOptimist                           0.0000000   0.0000000
## Q101162.fctrPessimist                          0.0000000   0.0000000
## Q101163.fctrDad                                0.0000000   0.0000000
## Q101163.fctrMom                                0.0000000   0.0000000
## Q101596.fctrNo                                 0.0000000   0.0000000
## Q101596.fctrYes                                0.0000000   0.0000000
## Q102089.fctrOwn                                0.0000000   0.0000000
## Q102089.fctrRent                               0.0000000   0.0000000
## Q102289.fctrNo                                 0.0000000   0.0000000
## Q102289.fctrYes                                0.0000000   0.0000000
## Q102674.fctrNo                                 0.0000000   0.0000000
## Q102674.fctrYes                                0.0000000   0.0000000
## Q102687.fctrNo                                 0.0000000   0.0000000
## Q102687.fctrYes                                0.0000000   0.0000000
## Q102906.fctrNo                                 0.0000000   0.0000000
## Q102906.fctrYes                                0.0000000   0.0000000
## Q103293.fctrNo                                 0.0000000   0.0000000
## Q103293.fctrYes                                0.0000000   0.0000000
## Q104996.fctrNo                                 0.0000000   0.0000000
## Q104996.fctrYes                                0.0000000   0.0000000
## Q105655.fctrNo                                 0.0000000   0.0000000
## Q105655.fctrYes                                0.0000000   0.0000000
## Q105840.fctrNo                                 0.0000000   0.0000000
## Q105840.fctrYes                                0.0000000   0.0000000
## Q106042.fctrNo                                 0.0000000   0.0000000
## Q106042.fctrYes                                0.0000000   0.0000000
## Q106272.fctrNo                                 0.0000000   0.0000000
## Q106272.fctrYes                                0.0000000   0.0000000
## Q106388.fctrNo                                 0.0000000   0.0000000
## Q106388.fctrYes                                0.0000000   0.0000000
## Q106389.fctrNo                                 0.0000000   0.0000000
## Q106389.fctrYes                                0.0000000   0.0000000
## Q106993.fctrNo                                 0.0000000   0.0000000
## Q106993.fctrYes                                0.0000000   0.0000000
## Q106997.fctrGr                                 0.0000000   0.0000000
## Q106997.fctrYy                                 0.0000000   0.0000000
## Q107491.fctrNo                                 0.0000000   0.0000000
## Q107491.fctrYes                                0.0000000   0.0000000
## Q107869.fctrNo                                 0.0000000   0.0000000
## Q107869.fctrYes                                0.0000000   0.0000000
## Q108342.fctrIn-person                          0.0000000   0.0000000
## Q108342.fctrOnline                             0.0000000   0.0000000
## Q108343.fctrNo                                 0.0000000   0.0000000
## Q108343.fctrYes                                0.0000000   0.0000000
## Q108617.fctrNo                                 0.0000000   0.0000000
## Q108617.fctrYes                                0.0000000   0.0000000
## Q108754.fctrNo                                 0.0000000   0.0000000
## Q108754.fctrYes                                0.0000000   0.0000000
## Q108855.fctrUmm...                             0.0000000   0.0000000
## Q108855.fctrYes!                               0.0000000   0.0000000
## Q108856.fctrSocialize                          0.0000000   0.0000000
## Q108856.fctrSpace                              0.0000000   0.0000000
## Q108950.fctrCautious                           0.0000000   0.0000000
## Q108950.fctrRisk-friendly                      0.0000000   0.0000000
## Q109367.fctrNo                                 0.0000000   0.0000000
## Q109367.fctrYes                                0.0000000   0.0000000
## Q110740.fctrPC                                 0.0000000   0.0000000
## Q111220.fctrNo                                 0.0000000   0.0000000
## Q111220.fctrYes                                0.0000000   0.0000000
## Q111580.fctrDemanding                          0.0000000   0.0000000
## Q111580.fctrSupportive                         0.0000000   0.0000000
## Q111848.fctrNo                                 0.0000000   0.0000000
## Q111848.fctrYes                                0.0000000   0.0000000
## Q112270.fctrYes                                0.0000000   0.0000000
## Q112478.fctrYes                                0.0000000   0.0000000
## Q112512.fctrNo                                 0.0000000   0.0000000
## Q112512.fctrYes                                0.0000000   0.0000000
## Q113181.fctrNo                                 0.0000000   0.0000000
## Q113181.fctrYes                                0.0000000   0.0000000
## Q113583.fctrTalk                               0.0000000   0.0000000
## Q113583.fctrTunes                              0.0000000   0.0000000
## Q113584.fctrPeople                             0.0000000   0.0000000
## Q113584.fctrTechnology                         0.0000000   0.0000000
## Q113992.fctrNo                                 0.0000000   0.0000000
## Q113992.fctrYes                                0.0000000   0.0000000
## Q114152.fctrNo                                 0.0000000   0.0000000
## Q114386.fctrMysterious                         0.0000000   0.0000000
## Q114386.fctrTMI                                0.0000000   0.0000000
## Q114517.fctrNo                                 0.0000000   0.0000000
## Q114748.fctrNo                                 0.0000000   0.0000000
## Q114748.fctrYes                                0.0000000   0.0000000
## Q114961.fctrNo                                 0.0000000   0.0000000
## Q114961.fctrYes                                0.0000000   0.0000000
## Q115195.fctrNo                                 0.0000000   0.0000000
## Q115195.fctrYes                                0.0000000   0.0000000
## Q115390.fctrNo                                 0.0000000   0.0000000
## Q115390.fctrYes                                0.0000000   0.0000000
## Q115602.fctrNo                                 0.0000000   0.0000000
## Q115602.fctrYes                                0.0000000   0.0000000
## Q115610.fctrNo                                 0.0000000   0.0000000
## Q115610.fctrYes                                0.0000000   0.0000000
## Q115611.fctrNo                                 0.0000000   0.0000000
## Q115777.fctrEnd                                0.0000000   0.0000000
## Q115777.fctrStart                              0.0000000   0.0000000
## Q115899.fctrCs                                 0.0000000   0.0000000
## Q115899.fctrMe                                 0.0000000   0.0000000
## Q116441.fctrNo                                 0.0000000   0.0000000
## Q116441.fctrYes                                0.0000000   0.0000000
## Q116448.fctrNo                                 0.0000000   0.0000000
## Q116448.fctrYes                                0.0000000   0.0000000
## Q116601.fctrNo                                 0.0000000   0.0000000
## Q116601.fctrYes                                0.0000000   0.0000000
## Q116797.fctrNo                                 0.0000000   0.0000000
## Q116797.fctrYes                                0.0000000   0.0000000
## Q116881.fctrHappy                              0.0000000   0.0000000
## Q116881.fctrRight                              0.0000000   0.0000000
## Q116953.fctrYes                                0.0000000   0.0000000
## Q117186.fctrHot headed                         0.0000000   0.0000000
## Q117193.fctrOdd hours                          0.0000000   0.0000000
## Q117193.fctrStandard hours                     0.0000000   0.0000000
## Q118117.fctrNo                                 0.0000000   0.0000000
## Q118117.fctrYes                                0.0000000   0.0000000
## Q118232.fctrPr                                 0.0000000   0.0000000
## Q118233.fctrNo                                 0.0000000   0.0000000
## Q118233.fctrYes                                0.0000000   0.0000000
## Q118237.fctrNo                                 0.0000000   0.0000000
## Q118237.fctrYes                                0.0000000   0.0000000
## Q118892.fctrNo                                 0.0000000   0.0000000
## Q118892.fctrYes                                0.0000000   0.0000000
## Q119334.fctrNo                                 0.0000000   0.0000000
## Q119334.fctrYes                                0.0000000   0.0000000
## Q119650.fctrGiving                             0.0000000   0.0000000
## Q119650.fctrReceiving                          0.0000000   0.0000000
## Q119851.fctrYes                                0.0000000   0.0000000
## Q120012.fctrNo                                 0.0000000   0.0000000
## Q120012.fctrYes                                0.0000000   0.0000000
## Q120014.fctrNo                                 0.0000000   0.0000000
## Q120014.fctrYes                                0.0000000   0.0000000
## Q120379.fctrNo                                 0.0000000   0.0000000
## Q120379.fctrYes                                0.0000000   0.0000000
## Q120472.fctrArt                                0.0000000   0.0000000
## Q120472.fctrScience                            0.0000000   0.0000000
## Q120650.fctrNo                                 0.0000000   0.0000000
## Q120650.fctrYes                                0.0000000   0.0000000
## Q120978.fctrNo                                 0.0000000   0.0000000
## Q120978.fctrYes                                0.0000000   0.0000000
## Q121011.fctrNo                                 0.0000000   0.0000000
## Q121011.fctrYes                                0.0000000   0.0000000
## Q121699.fctrYes                                0.0000000   0.0000000
## Q121700.fctrNo                                 0.0000000   0.0000000
## Q121700.fctrYes                                0.0000000   0.0000000
## Q122120.fctrNo                                 0.0000000   0.0000000
## Q122120.fctrYes                                0.0000000   0.0000000
## Q122769.fctrNo                                 0.0000000   0.0000000
## Q122769.fctrYes                                0.0000000   0.0000000
## Q122770.fctrNo                                 0.0000000   0.0000000
## Q122770.fctrYes                                0.0000000   0.0000000
## Q122771.fctrPc                                 0.0000000   0.0000000
## Q122771.fctrPt                                 0.0000000   0.0000000
## Q123464.fctrNo                                 0.0000000   0.0000000
## Q123464.fctrYes                                0.0000000   0.0000000
## Q123621.fctrNo                                 0.0000000   0.0000000
## Q123621.fctrYes                                0.0000000   0.0000000
## Q124122.fctrNo                                 0.0000000   0.0000000
## Q124742.fctrYes                                0.0000000   0.0000000
## Q96024.fctrNo                                  0.0000000   0.0000000
## Q96024.fctrYes                                 0.0000000   0.0000000
## Q98059.fctrOnly-child                          0.0000000   0.0000000
## Q98059.fctrYes                                 0.0000000   0.0000000
## Q98078.fctrNo                                  0.0000000   0.0000000
## Q98078.fctrYes                                 0.0000000   0.0000000
## Q98197.fctrYes                                 0.0000000   0.0000000
## Q98578.fctrNo                                  0.0000000   0.0000000
## Q98578.fctrYes                                 0.0000000   0.0000000
## Q98869.fctrNo                                  0.0000000   0.0000000
## Q98869.fctrYes                                 0.0000000   0.0000000
## Q99480.fctrNo                                  0.0000000   0.0000000
## Q99480.fctrYes                                 0.0000000   0.0000000
## Q99581.fctrNo                                  0.0000000   0.0000000
## Q99581.fctrYes                                 0.0000000   0.0000000
## Q99716.fctrNo                                  0.0000000   0.0000000
## Q99716.fctrYes                                 0.0000000   0.0000000
## Q99982.fctrCheck!                              0.0000000   0.0000000
## Q99982.fctrNope                                0.0000000   0.0000000
## YOB.Age.fctr(20,25]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(25,30]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(30,35]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(35,40]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(40,50]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(50,65]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr(65,90]:YOB.Age.dff                0.0000000   0.0000000
## YOB.Age.fctr.C                                 0.0000000   0.0000000
## YOB.Age.fctr.L                                 0.0000000   0.0000000
## YOB.Age.fctr.Q                                 0.0000000   0.0000000
## YOB.Age.fctrNA:YOB.Age.dff                     0.0000000   0.0000000
## YOB.Age.fctr^4                                 0.0000000   0.0000000
## YOB.Age.fctr^5                                 0.0000000   0.0000000
## YOB.Age.fctr^6                                 0.0000000   0.0000000
## YOB.Age.fctr^8                                 0.0000000   0.0000000
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    5638          R                         0.1432618
## 2    4506          R                         0.1402091
## 3     468          R                         0.1568337
## 4    3212          R                                NA
## 5     626          R                                NA
## 6    4785          R                         0.1709531
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                         <NA>                               NA
## 5                         <NA>                               NA
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.8567382                               FALSE
## 2                            0.8597909                               FALSE
## 3                            0.8431663                               FALSE
## 4                                   NA                                  NA
## 5                                   NA                                  NA
## 6                            0.8290469                               FALSE
##   Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 1                            0.1501717                               D
## 2                            0.1508326                               D
## 3                            0.1541281                               D
## 4                            0.1546975                               D
## 5                            0.1561392                               D
## 6                            0.1593257                               D
##   Party.fctr.Final.All.X...glmnet.err
## 1                                TRUE
## 2                                TRUE
## 3                                TRUE
## 4                                TRUE
## 5                                TRUE
## 6                                TRUE
##   Party.fctr.Final.All.X...glmnet.err.abs
## 1                               0.8498283
## 2                               0.8491674
## 3                               0.8458719
## 4                               0.8453025
## 5                               0.8438608
## 6                               0.8406743
##   Party.fctr.Final.All.X...glmnet.is.acc
## 1                                  FALSE
## 2                                  FALSE
## 3                                  FALSE
## 4                                  FALSE
## 5                                  FALSE
## 6                                  FALSE
##   Party.fctr.Final.All.X...glmnet.accurate
## 1                                    FALSE
## 2                                    FALSE
## 3                                    FALSE
## 4                                    FALSE
## 5                                    FALSE
## 6                                    FALSE
##   Party.fctr.Final.All.X...glmnet.error
## 1                            -0.4498283
## 2                            -0.4491674
## 3                            -0.4458719
## 4                            -0.4453025
## 5                            -0.4438608
## 6                            -0.4406743
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 36     2638          R                         0.1690641
## 75     1487          R                         0.2121848
## 93     1569          R                         0.2816323
## 98     2428          R                         0.2111990
## 141    6197          R                         0.2428644
## 162    4364          R                         0.2927004
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 36                             D                             TRUE
## 75                             D                             TRUE
## 93                             D                             TRUE
## 98                             D                             TRUE
## 141                            D                             TRUE
## 162                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 36                             0.8309359
## 75                             0.7878152
## 93                             0.7183677
## 98                             0.7888010
## 141                            0.7571356
## 162                            0.7072996
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 36                                FALSE
## 75                                FALSE
## 93                                FALSE
## 98                                FALSE
## 141                               FALSE
## 162                               FALSE
##     Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 36                             0.1882236                               D
## 75                             0.2058497                               D
## 93                             0.2166311                               D
## 98                             0.2188063                               D
## 141                            0.2384300                               D
## 162                            0.2549770                               D
##     Party.fctr.Final.All.X...glmnet.err
## 36                                 TRUE
## 75                                 TRUE
## 93                                 TRUE
## 98                                 TRUE
## 141                                TRUE
## 162                                TRUE
##     Party.fctr.Final.All.X...glmnet.err.abs
## 36                                0.8117764
## 75                                0.7941503
## 93                                0.7833689
## 98                                0.7811937
## 141                               0.7615700
## 162                               0.7450230
##     Party.fctr.Final.All.X...glmnet.is.acc
## 36                                   FALSE
## 75                                   FALSE
## 93                                   FALSE
## 98                                   FALSE
## 141                                  FALSE
## 162                                  FALSE
##     Party.fctr.Final.All.X...glmnet.accurate
## 36                                     FALSE
## 75                                     FALSE
## 93                                     FALSE
## 98                                     FALSE
## 141                                    FALSE
## 162                                    FALSE
##     Party.fctr.Final.All.X...glmnet.error
## 36                             -0.4117764
## 75                             -0.3941503
## 93                             -0.3833689
## 98                             -0.3811937
## 141                            -0.3615700
## 162                            -0.3450230
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 178     749          R                         0.3048864
## 179    3291          R                         0.2948194
## 180    5291          R                         0.2758853
## 181    1482          R                         0.5202509
## 182    4552          R                         0.6715854
## 183    5144          R                         0.6827682
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 178                            D                             TRUE
## 179                            D                             TRUE
## 180                            D                             TRUE
## 181                            D                             TRUE
## 182                            R                            FALSE
## 183                            R                            FALSE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 178                            0.6951136
## 179                            0.7051806
## 180                            0.7241147
## 181                            0.4797491
## 182                            0.3284146
## 183                            0.3172318
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 178                               FALSE
## 179                               FALSE
## 180                               FALSE
## 181                               FALSE
## 182                                TRUE
## 183                                TRUE
##     Party.fctr.Final.All.X...glmnet.prob Party.fctr.Final.All.X...glmnet
## 178                            0.2740269                               D
## 179                            0.2749663                               D
## 180                            0.2847975                               D
## 181                            0.3618322                               D
## 182                            0.4416022                               D
## 183                            0.4534200                               D
##     Party.fctr.Final.All.X...glmnet.err
## 178                                TRUE
## 179                                TRUE
## 180                                TRUE
## 181                                TRUE
## 182                                TRUE
## 183                                TRUE
##     Party.fctr.Final.All.X...glmnet.err.abs
## 178                               0.7259731
## 179                               0.7250337
## 180                               0.7152025
## 181                               0.6381678
## 182                               0.5583978
## 183                               0.5465800
##     Party.fctr.Final.All.X...glmnet.is.acc
## 178                                  FALSE
## 179                                  FALSE
## 180                                  FALSE
## 181                                  FALSE
## 182                                  FALSE
## 183                                  FALSE
##     Party.fctr.Final.All.X...glmnet.accurate
## 178                                    FALSE
## 179                                    FALSE
## 180                                    FALSE
## 181                                    FALSE
## 182                                    FALSE
## 183                                    FALSE
##     Party.fctr.Final.All.X...glmnet.error
## 178                            -0.3259731
## 179                            -0.3250337
## 180                            -0.3152025
## 181                            -0.2381678
## 182                            -0.1583978
## 183                            -0.1465800

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final.All.X...glmnet.prob"   
## [2] "Party.fctr.Final.All.X...glmnet"        
## [3] "Party.fctr.Final.All.X...glmnet.err"    
## [4] "Party.fctr.Final.All.X...glmnet.err.abs"
## [5] "Party.fctr.Final.All.X...glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 9  fit.data.training          5          1           1 190.474 196.152
## 10  predict.data.new          6          0           0 196.152      NA
##    elapsed
## 9    5.678
## 10      NA

Step 6.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.6
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## Warning: Removed 223 rows containing missing values (geom_point).

## NULL
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] "Stacking file Q109244NA_Ensemble_cnk03_rest_out_fin.csv to prediction outputs..."
## [1] 0.6
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final.All.X###glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet       Max.cor.Y##rcv#rpart 
##                          0                          1 
##       Final.All.X###glmnet 
##                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet                      0.7875648       0.5511874
## Max.cor.Y.rcv.1X1###glmnet             0.7875648       0.5478979
## Low.cor.X##rcv#glmnet                  0.7875648       0.5465340
## Random###myrandom_classfr              0.7875648       0.5012035
## MFO###myMFO_classfr                    0.7875648       0.5000000
## Max.cor.Y##rcv#rpart                   0.7875648       0.5000000
## Interact.High.cor.Y##rcv#glmnet        0.7875648       0.4963094
## Final.All.X###glmnet                          NA              NA
##                                 max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet                     0.4967105                     22.489
## Max.cor.Y.rcv.1X1###glmnet            0.5000000                      0.788
## Low.cor.X##rcv#glmnet                 0.4967105                     26.852
## Random###myrandom_classfr             0.4922978                      0.267
## MFO###myMFO_classfr                   0.5000000                      0.429
## Max.cor.Y##rcv#rpart                  0.5000000                      1.576
## Interact.High.cor.Y##rcv#glmnet       0.5000000                      1.724
## Final.All.X###glmnet                         NA                      2.429
##                                 max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet                      0.8069228                   0.25
## Max.cor.Y.rcv.1X1###glmnet             0.8060109                   0.50
## Low.cor.X##rcv#glmnet                  0.8069191                   0.30
## Random###myrandom_classfr              0.8060109                   0.85
## MFO###myMFO_classfr                    0.8060109                   0.50
## Max.cor.Y##rcv#rpart                   0.8060121                   0.50
## Interact.High.cor.Y##rcv#glmnet        0.8060121                   0.50
## Final.All.X###glmnet                   0.8075676                   0.25
##                                 opt.prob.threshold.OOB
## All.X##rcv#glmnet                                 0.60
## Max.cor.Y.rcv.1X1###glmnet                        0.50
## Low.cor.X##rcv#glmnet                             0.65
## Random###myrandom_classfr                         0.85
## MFO###myMFO_classfr                               0.50
## Max.cor.Y##rcv#rpart                              0.50
## Interact.High.cor.Y##rcv#glmnet                   0.50
## Final.All.X###glmnet                                NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   D   R
##         D 152   0
##         R  41   0
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        1.415925        1.243398        2.711939              NA
## N         10.618246        3.510021       14.553264              NA
## MKy       52.182641       15.160971       68.876543              NA
## SKy        9.444703        2.878612       12.628212              NA
## MKn       31.035907        7.342244       39.517054              NA
## SKn      105.501365       29.134241      135.798491              NA
## PKn        8.686566        2.779787       12.238816              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.OOB
## PKy     0.01092896     0.01554404     0.00896861      8        2      3
## N       0.05464481     0.04663212     0.04484305     40       10      9
## MKy     0.24863388     0.24352332     0.24663677    182       55     47
## SKy     0.05054645     0.04663212     0.04484305     37       10      9
## MKn     0.13661202     0.11917098     0.11659193    100       26     23
## SKn     0.44262295     0.48186528     0.49327354    324      110     93
## PKn     0.05601093     0.04663212     0.04484305     41       10      9
##     .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy       10        1      2      8      2     11        0.4144660
## N         40        9     10     40     10     49        0.3900023
## MKy      186       43     55    182     55    229        0.3225738
## SKy       39        7     10     37     10     46        0.3198458
## MKn       97       26     26    100     26    123        0.3192280
## SKn      325       92    110    324    110    417        0.3132714
## PKn       45        5     10     41     10     50        0.3088652
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.1769906               NA        0.2465399
## N          0.2654561               NA        0.2970054
## MKy        0.2867178               NA        0.3007709
## SKy        0.2552622               NA        0.2745263
## MKn        0.3103591               NA        0.3212769
## SKn        0.3256215               NA        0.3256559
## PKn        0.2118675               NA        0.2447763
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##       218.885353        62.049273       286.324318               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000       732.000000 
##         .n.New.D           .n.OOB         .n.Trn.D         .n.Trn.R 
##       223.000000       193.000000       742.000000       183.000000 
##           .n.Tst           .n.fit           .n.new           .n.trn 
##       223.000000       732.000000       223.000000       925.000000 
## err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean err.abs.trn.mean 
##         2.388253         1.832275               NA         2.010552
## [1] "Features Importance for selected models:"
##                              All.X..rcv.glmnet.imp
## Hhold.fctrN:.clusterid.fctr6            100.000000
## Edn.fctr.Q                               17.453730
## Q98197.fctrNo                            14.402766
## Q118232.fctrId                           12.858478
## Edn.fctr^5                               12.015604
## Q120194.fctrStudy first                   6.689618
## Q112478.fctrNo                            6.116418
##                              Final.All.X...glmnet.imp
## Hhold.fctrN:.clusterid.fctr6                100.00000
## Edn.fctr.Q                                   16.76565
## Q98197.fctrNo                                13.27072
## Q118232.fctrId                               29.41524
## Edn.fctr^5                                   16.06488
## Q120194.fctrStudy first                      16.66455
## Q112478.fctrNo                               12.60934
## [1] "glbObsNew prediction stats:"
## 
##   D   R 
## 223   0
##                   label step_major step_minor label_minor     bgn     end
## 10     predict.data.new          6          0           0 196.152 205.837
## 11 display.session.info          7          0           0 205.837      NA
##    elapsed
## 10   9.685
## 11      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                      label step_major step_minor label_minor     bgn
## 4               fit.models          4          0           0  51.324
## 5               fit.models          4          1           1 131.607
## 1             cluster.data          1          0           0   9.273
## 2  partition.data.training          2          0           0  29.707
## 10        predict.data.new          6          0           0 196.152
## 8        fit.data.training          5          0           0 181.847
## 6               fit.models          4          2           2 171.471
## 9        fit.data.training          5          1           1 190.474
## 7               fit.models          4          3           3 178.930
## 3          select.features          3          0           0  48.833
##        end elapsed duration
## 4  131.606  80.282   80.282
## 5  171.470  39.863   39.863
## 1   29.707  20.434   20.434
## 2   48.832  19.125   19.125
## 10 205.837   9.685    9.685
## 8  190.473   8.627    8.626
## 6  178.930   7.459    7.459
## 9  196.152   5.678    5.678
## 7  181.846   2.916    2.916
## 3   51.324   2.491    2.491
## [1] "Total Elapsed Time: 205.837 secs"

##                              label step_major step_minor      label_minor
## 6           fit.models_0_Low.cor.X          1          5           glmnet
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4           glmnet
## 2                 fit.models_0_MFO          1          1    myMFO_classfr
## 3              fit.models_0_Random          1          2 myrandom_classfr
## 1                 fit.models_0_bgn          1          0            setup
##      bgn     end elapsed duration
## 6 93.830 131.592  37.763   37.762
## 4 67.086  84.218  17.132   17.132
## 5 84.219  93.830   9.611    9.611
## 2 51.886  59.540   7.654    7.654
## 3 59.541  67.085   7.544    7.544
## 1 51.853  51.886   0.033    0.033
## [1] "Total Elapsed Time: 131.592 secs"